{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.53736392, 4.52251   ],\n",
       "       [0.41032424, 2.59142764],\n",
       "       [1.02541721, 4.74055523],\n",
       "       [0.2282823 , 1.5813632 ],\n",
       "       [2.67285074, 4.45203978],\n",
       "       [4.75055015, 3.60037654],\n",
       "       [4.92160603, 2.94978755],\n",
       "       [0.36860155, 4.06197765],\n",
       "       [4.17563135, 3.68364989],\n",
       "       [2.86247429, 3.78989322],\n",
       "       [2.54406693, 1.92508516],\n",
       "       [2.1197772 , 3.6185543 ],\n",
       "       [4.83274519, 3.31557201],\n",
       "       [2.62832849, 1.56980732],\n",
       "       [2.18883191, 2.69979481],\n",
       "       [1.48086909, 3.05127115],\n",
       "       [3.14619869, 4.61358278],\n",
       "       [4.0936665 , 1.19518022],\n",
       "       [1.8642058 , 3.89144154],\n",
       "       [1.15113738, 0.94576591],\n",
       "       [3.93578037, 0.02588325],\n",
       "       [3.97779687, 2.38333964],\n",
       "       [2.47533852, 3.70045363],\n",
       "       [0.96132498, 4.3283406 ],\n",
       "       [2.19576132, 0.27936364],\n",
       "       [1.81890596, 4.72005686],\n",
       "       [3.92290153, 4.02363304],\n",
       "       [1.94689373, 2.13306138],\n",
       "       [1.34669026, 0.6657542 ],\n",
       "       [2.69596608, 4.18264277],\n",
       "       [2.48451571, 1.92369117],\n",
       "       [1.55021773, 0.61480132],\n",
       "       [2.25475259, 1.52212272],\n",
       "       [3.97220096, 2.06515256],\n",
       "       [0.83074226, 3.10241724],\n",
       "       [4.22878591, 0.56306754],\n",
       "       [4.57002351, 1.15481474],\n",
       "       [4.64079016, 4.94930432],\n",
       "       [0.58272231, 2.59618408],\n",
       "       [2.58843784, 0.66292227],\n",
       "       [2.50633722, 4.32141592],\n",
       "       [4.93443844, 3.92294601],\n",
       "       [3.22840237, 1.72130516],\n",
       "       [3.01490093, 4.19788103],\n",
       "       [3.61369742, 1.47736076],\n",
       "       [4.17307936, 3.30448404],\n",
       "       [0.97365703, 4.32574889],\n",
       "       [2.12225477, 3.46058165],\n",
       "       [0.5088859 , 2.8316847 ],\n",
       "       [4.49673471, 0.41847216],\n",
       "       [1.29326565, 2.23434356],\n",
       "       [0.87096066, 2.86523929],\n",
       "       [4.49555844, 0.02419797],\n",
       "       [0.29851084, 4.90334459],\n",
       "       [1.26203199, 2.66014057],\n",
       "       [3.67607829, 4.54684364],\n",
       "       [4.84115446, 3.12168516],\n",
       "       [0.50095226, 0.55227266],\n",
       "       [3.412455  , 4.49199301],\n",
       "       [3.50334011, 0.1521472 ],\n",
       "       [0.16433001, 2.44137345],\n",
       "       [4.38794468, 0.74496395],\n",
       "       [1.08597627, 0.14724123],\n",
       "       [0.75478834, 1.71072747],\n",
       "       [2.44259739, 3.96792691],\n",
       "       [2.78429688, 1.66829969],\n",
       "       [4.65462502, 1.54373262],\n",
       "       [4.74216912, 4.74789329],\n",
       "       [4.89917798, 4.74562654],\n",
       "       [3.19822797, 2.85343934],\n",
       "       [4.20445002, 2.67911041],\n",
       "       [2.33744087, 1.60462259],\n",
       "       [4.44414488, 4.60013802],\n",
       "       [1.90651028, 1.77170539],\n",
       "       [0.45174523, 3.7313651 ],\n",
       "       [4.27164503, 4.23886491],\n",
       "       [2.93810751, 3.11623099],\n",
       "       [2.43267956, 1.55218874],\n",
       "       [4.92291879, 4.07545135],\n",
       "       [1.00366932, 0.84755583],\n",
       "       [4.51091765, 4.05490542],\n",
       "       [4.37780206, 2.20329383],\n",
       "       [4.45278874, 2.06249239],\n",
       "       [4.30876597, 3.41627558],\n",
       "       [2.07534926, 4.58021643],\n",
       "       [0.99230951, 0.13008075],\n",
       "       [1.07365495, 2.60773876],\n",
       "       [2.42328767, 3.5859732 ],\n",
       "       [1.21178974, 1.93582746],\n",
       "       [3.9726016 , 1.91536208],\n",
       "       [3.64966694, 3.06635631],\n",
       "       [0.03192771, 1.75104557],\n",
       "       [3.03296892, 3.15822677],\n",
       "       [3.36678952, 3.39564123],\n",
       "       [4.67250202, 1.50242155],\n",
       "       [0.80046142, 0.33030305],\n",
       "       [2.50756703, 0.69694034],\n",
       "       [1.45166018, 1.1803252 ],\n",
       "       [4.74454865, 2.89696372],\n",
       "       [2.61122716, 1.45613442]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "array1 = np.random.uniform(0.0,5.0,size= (100,2))#从均匀分布中随机采样 ，产生100个[0.0,0.5）2列的数组\n",
    "array1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.08667192, 5.91178585],\n",
       "       [9.4139723 , 9.12409453],\n",
       "       [5.81462968, 5.15540372],\n",
       "       [9.07513757, 9.86099899],\n",
       "       [9.35544727, 8.05019383],\n",
       "       [7.5554148 , 4.77845029],\n",
       "       [7.9124602 , 4.78072497],\n",
       "       [9.54063686, 8.97907908],\n",
       "       [4.52083662, 6.51211875],\n",
       "       [8.76554929, 5.163063  ],\n",
       "       [9.32791722, 4.40278942],\n",
       "       [5.19256332, 9.94551156],\n",
       "       [8.71533625, 6.3514251 ],\n",
       "       [7.59672252, 6.08117152],\n",
       "       [9.85671742, 4.81806876],\n",
       "       [5.79922546, 4.21296612],\n",
       "       [9.39680768, 7.64984973],\n",
       "       [9.51620043, 7.13475114],\n",
       "       [8.46032591, 7.58212579],\n",
       "       [6.57484241, 9.11081713],\n",
       "       [7.00661497, 8.30975356],\n",
       "       [5.49731563, 8.55912953],\n",
       "       [6.26895212, 7.01725285],\n",
       "       [7.94966206, 8.29071497],\n",
       "       [9.32582513, 4.69518622],\n",
       "       [9.1151587 , 9.57317945],\n",
       "       [6.54555361, 8.72593382],\n",
       "       [9.0079882 , 6.75993709],\n",
       "       [7.39418824, 9.82724997],\n",
       "       [9.98626146, 5.84522165],\n",
       "       [8.42239187, 4.43267398],\n",
       "       [6.81867529, 7.9683964 ],\n",
       "       [8.31478588, 6.61412587],\n",
       "       [6.09796296, 5.84557955],\n",
       "       [4.40187247, 6.46331048],\n",
       "       [5.30580533, 5.1328029 ],\n",
       "       [8.87148703, 6.53231748],\n",
       "       [8.72209974, 4.38238595],\n",
       "       [8.23275833, 8.29047001],\n",
       "       [6.57408624, 6.50794182],\n",
       "       [9.40539457, 7.11459935],\n",
       "       [7.78795888, 5.67084357],\n",
       "       [5.90972657, 7.51954658],\n",
       "       [4.50469588, 5.96367087],\n",
       "       [6.26622463, 7.4745771 ],\n",
       "       [7.1057646 , 9.13569557],\n",
       "       [6.22545278, 6.70378994],\n",
       "       [7.47113687, 4.58979511],\n",
       "       [8.87714972, 5.86322075],\n",
       "       [4.29493379, 9.02570321],\n",
       "       [8.35825808, 4.66699224],\n",
       "       [8.08359073, 9.66883998],\n",
       "       [4.33855586, 8.46429959],\n",
       "       [8.71378083, 9.88127392],\n",
       "       [8.13984236, 9.47143378],\n",
       "       [7.34687619, 5.6145813 ],\n",
       "       [8.41138498, 9.24959197],\n",
       "       [6.49443322, 9.79352547],\n",
       "       [8.55352295, 5.68917004],\n",
       "       [5.87013293, 7.78764107],\n",
       "       [4.80364307, 9.94307536],\n",
       "       [6.01849615, 5.73594875],\n",
       "       [6.42465152, 7.40656265],\n",
       "       [9.61884464, 8.07609966],\n",
       "       [8.0790608 , 7.27046931],\n",
       "       [7.84679845, 5.42636086],\n",
       "       [6.28382559, 6.30582252],\n",
       "       [8.81563132, 9.27544296],\n",
       "       [9.05502716, 5.79557294],\n",
       "       [7.73492098, 5.08838645],\n",
       "       [7.96146625, 4.20605231],\n",
       "       [5.77519435, 5.42963046],\n",
       "       [7.1657782 , 4.33205886],\n",
       "       [4.6193646 , 5.11778003],\n",
       "       [8.08253133, 5.56352488],\n",
       "       [7.82381512, 7.10705372],\n",
       "       [5.12535272, 9.98986946],\n",
       "       [4.67901365, 7.95505414],\n",
       "       [9.63868517, 4.69401924],\n",
       "       [5.42807859, 5.78556101],\n",
       "       [9.83360946, 4.75618104],\n",
       "       [6.99163594, 8.97844065],\n",
       "       [5.48163059, 7.85640731],\n",
       "       [4.85429321, 8.90641897],\n",
       "       [8.1387416 , 9.02346163],\n",
       "       [8.95686061, 7.81961544],\n",
       "       [5.48639029, 8.72662502],\n",
       "       [4.47200712, 5.15918268],\n",
       "       [5.00593387, 6.49582259],\n",
       "       [6.22911924, 7.84416909],\n",
       "       [4.59961509, 5.11644609],\n",
       "       [8.90239818, 4.44367493],\n",
       "       [5.37037443, 6.2124214 ],\n",
       "       [8.19789474, 4.33569552],\n",
       "       [7.68945073, 7.44799477],\n",
       "       [9.24182499, 5.19111408],\n",
       "       [9.04593081, 8.70734149],\n",
       "       [9.6175825 , 8.21315614],\n",
       "       [9.09028407, 9.69894601],\n",
       "       [4.27950168, 9.02029359]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制第二种类型散点图\n",
    "array2 = np.random.uniform(4.2,10,size= (100,2))#从均匀分布中随机采样 ，产生100个[4.2,10）2列的数组\n",
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存\n",
    "np.savetxt('./matplot_test_data1.txt',array1,fmt='%f %f',delimiter='\\n')  #fmt 格式，delimiter 分割符\n",
    "np.savetxt('./matplot_test_data2.txt',array2,fmt='%f %f',delimiter='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载\n",
    "a = np.loadtxt('./matplot_test_data1.txt')\n",
    "b = np.loadtxt('./matplot_test_data2.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26f12317550>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示，第一个参数：所有数据，第二个参数行或列\n",
    "plt.scatter(a[: ,0],a[: ,1])\n",
    "plt.scatter(b[: ,0],b[: ,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看行列\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26f12546940>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可以修改点的大小，点的颜色,形状\n",
    "x = [1,2,3,4]\n",
    "y = [2,3,4,8]\n",
    "plt.scatter(x,y)\n",
    "plt.scatter(x,y,s=[20,40,80,300] ,color=['r','g','b','y'],marker = 'o') #color='#ff0000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([196.21441217, 178.68479522, 197.55384584, 157.02722986,\n",
       "       185.08146991, 168.06944665, 147.00175387, 198.16951371,\n",
       "       174.28671814, 142.78902955, 188.41167206, 162.5235012 ,\n",
       "       181.28435489, 153.41562282, 164.87412244, 189.68025965,\n",
       "       175.45781196, 149.20255074, 142.70449172, 179.36693768])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子 男女身高\n",
    "male_high = np.random.uniform(140,200,size = 20) #生成20个140到200的随机数\n",
    "male_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_id = np.arange(21)\n",
    "male_id = np.delete(male_id,0)  #删除id 0\n",
    "male_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[196.21441217],\n",
       "       [178.68479522],\n",
       "       [197.55384584],\n",
       "       [157.02722986],\n",
       "       [185.08146991],\n",
       "       [168.06944665],\n",
       "       [147.00175387],\n",
       "       [198.16951371],\n",
       "       [174.28671814],\n",
       "       [142.78902955],\n",
       "       [188.41167206],\n",
       "       [162.5235012 ],\n",
       "       [181.28435489],\n",
       "       [153.41562282],\n",
       "       [164.87412244],\n",
       "       [189.68025965],\n",
       "       [175.45781196],\n",
       "       [149.20255074],\n",
       "       [142.70449172],\n",
       "       [179.36693768]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把行数据变成列数据，再把数据进行合并\n",
    "male_high  = male_high.reshape(20,1)  #把malehigh，20个数据，形成1列\n",
    "male_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10],\n",
       "       [11],\n",
       "       [12],\n",
       "       [13],\n",
       "       [14],\n",
       "       [15],\n",
       "       [16],\n",
       "       [17],\n",
       "       [18],\n",
       "       [19],\n",
       "       [20]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_id  = male_id.reshape(20,1)  #把maleid，20个数据，形成1列\n",
    "male_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 196.21441217],\n",
       "       [  2.        , 178.68479522],\n",
       "       [  3.        , 197.55384584],\n",
       "       [  4.        , 157.02722986],\n",
       "       [  5.        , 185.08146991],\n",
       "       [  6.        , 168.06944665],\n",
       "       [  7.        , 147.00175387],\n",
       "       [  8.        , 198.16951371],\n",
       "       [  9.        , 174.28671814],\n",
       "       [ 10.        , 142.78902955],\n",
       "       [ 11.        , 188.41167206],\n",
       "       [ 12.        , 162.5235012 ],\n",
       "       [ 13.        , 181.28435489],\n",
       "       [ 14.        , 153.41562282],\n",
       "       [ 15.        , 164.87412244],\n",
       "       [ 16.        , 189.68025965],\n",
       "       [ 17.        , 175.45781196],\n",
       "       [ 18.        , 149.20255074],\n",
       "       [ 19.        , 142.70449172],\n",
       "       [ 20.        , 179.36693768]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进行合并\n",
    "male = np.concatenate((male_id,male_high),axis=1)\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 184.38289271],\n",
       "       [  2.        , 183.75073211],\n",
       "       [  3.        , 155.52747435],\n",
       "       [  4.        , 163.27931042],\n",
       "       [  5.        , 143.21587716],\n",
       "       [  6.        , 152.14462102],\n",
       "       [  7.        , 150.17285821],\n",
       "       [  8.        , 174.49566283],\n",
       "       [  9.        , 189.48120746],\n",
       "       [ 10.        , 150.18154181],\n",
       "       [ 11.        , 147.24981528],\n",
       "       [ 12.        , 151.82879879],\n",
       "       [ 13.        , 192.89455687],\n",
       "       [ 14.        , 179.94959639],\n",
       "       [ 15.        , 171.84668731],\n",
       "       [ 16.        , 174.25506578],\n",
       "       [ 17.        , 185.83171857],\n",
       "       [ 18.        , 184.01683406],\n",
       "       [ 19.        , 186.63160041],\n",
       "       [ 20.        , 196.20046975]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成女性数据\n",
    "female_high = np.random.uniform(140,200,size = 20) #生成20个140到200的随机数\n",
    "female_id = np.arange(21)\n",
    "female_id = np.delete(female_id,0)  #删除id 0\n",
    "female_high  = female_high.reshape(20,1)  #把malehigh，20个数据，形成1列\n",
    "female_id  = female_id.reshape(20,1)  #把maleid，20个数据，形成1列\n",
    "female = np.concatenate((female_id,female_high),axis=1)\n",
    "female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 196.21441217],\n",
       "       [  2.        , 178.68479522],\n",
       "       [  3.        , 197.55384584],\n",
       "       [  4.        , 157.02722986],\n",
       "       [  5.        , 185.08146991],\n",
       "       [  6.        , 168.06944665],\n",
       "       [  7.        , 147.00175387],\n",
       "       [  8.        , 198.16951371],\n",
       "       [  9.        , 174.28671814],\n",
       "       [ 10.        , 142.78902955],\n",
       "       [ 11.        , 188.41167206],\n",
       "       [ 12.        , 162.5235012 ],\n",
       "       [ 13.        , 181.28435489],\n",
       "       [ 14.        , 153.41562282],\n",
       "       [ 15.        , 164.87412244],\n",
       "       [ 16.        , 189.68025965],\n",
       "       [ 17.        , 175.45781196],\n",
       "       [ 18.        , 149.20255074],\n",
       "       [ 19.        , 142.70449172],\n",
       "       [ 20.        , 179.36693768],\n",
       "       [  1.        , 184.38289271],\n",
       "       [  2.        , 183.75073211],\n",
       "       [  3.        , 155.52747435],\n",
       "       [  4.        , 163.27931042],\n",
       "       [  5.        , 143.21587716],\n",
       "       [  6.        , 152.14462102],\n",
       "       [  7.        , 150.17285821],\n",
       "       [  8.        , 174.49566283],\n",
       "       [  9.        , 189.48120746],\n",
       "       [ 10.        , 150.18154181],\n",
       "       [ 11.        , 147.24981528],\n",
       "       [ 12.        , 151.82879879],\n",
       "       [ 13.        , 192.89455687],\n",
       "       [ 14.        , 179.94959639],\n",
       "       [ 15.        , 171.84668731],\n",
       "       [ 16.        , 174.25506578],\n",
       "       [ 17.        , 185.83171857],\n",
       "       [ 18.        , 184.01683406],\n",
       "       [ 19.        , 186.63160041],\n",
       "       [ 20.        , 196.20046975]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#男女数据合并成1列\n",
    "x = np.concatenate((male,female),axis = 0) #样本数据\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每个身高代表的男女\n",
    "y = np.concatenate([np.ones(20),np.zeros(20)])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 196.21441217],\n",
       "       [  2.        , 178.68479522],\n",
       "       [  3.        , 197.55384584],\n",
       "       [  4.        , 157.02722986],\n",
       "       [  5.        , 185.08146991],\n",
       "       [  6.        , 168.06944665],\n",
       "       [  7.        , 147.00175387],\n",
       "       [  8.        , 198.16951371],\n",
       "       [  9.        , 174.28671814],\n",
       "       [ 10.        , 142.78902955],\n",
       "       [ 11.        , 188.41167206],\n",
       "       [ 12.        , 162.5235012 ],\n",
       "       [ 13.        , 181.28435489],\n",
       "       [ 14.        , 153.41562282],\n",
       "       [ 15.        , 164.87412244],\n",
       "       [ 16.        , 189.68025965],\n",
       "       [ 17.        , 175.45781196],\n",
       "       [ 18.        , 149.20255074],\n",
       "       [ 19.        , 142.70449172],\n",
       "       [ 20.        , 179.36693768]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y==1 #如果等于1输出True 等于0输出FALSE\n",
    "#x[[True,True]]#错误的，需要填入40个才行\n",
    "x[y==1]  #输出男的身高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26f125f0898>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#取男的值出来\n",
    "plt.scatter(x[y==1,0],x[y==1,1],color = 'r')\n",
    "plt.scatter(x[y==0,0],x[y==0,1],color = 'g')\n",
    "plt.show() #只输出图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#折线图\n",
    "x = np.arange(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.17022005e-01, 7.20324493e-01, 1.14374817e-04, 3.02332573e-01,\n",
       "       1.46755891e-01, 9.23385948e-02, 1.86260211e-01, 3.45560727e-01,\n",
       "       3.96767474e-01, 5.38816734e-01])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "y = np.random.random(size=10)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VUZRSMejs/BwtLVcxPLzf6igxS+uCfvr0c4yOdqbsUEWlVHo6fvxhjh37GlVVH06rDd7TuqAXFV3PVVe9QFnZu62OEhOv9xF27bo+qXsMKqXmZmBgBwcP3kdJydtZtuxbKT16bqq0LugiQmHhupQdQjRVJDLK0NBzBIO9VkdRSkXh93fQ2vpe8vNfw+rV27DZ0usxY9oW9KNHv057+0aMCVsdJWbnRrroUrpKpaa8vDoWLXqANWt+icORb3WcOUvLgh6JhDh+/FuMjnYxvuVpenC7GwB0gpFSKSYUOs3o6DFEbNTWfh6Xq9rqSJckrQq6z7eV5uZa/vM/nQQC3bjdqT2yZSqnswKHo1QLulIpJBIJ0tp6G7t3X5/2S1ynTUH3+bbS3r6RQKCLczvg9fRswefbam2wORARysreg8u1yOooSinGN8U5cOAvOXnyN9TWfhG7Pc/qSJclbe74d3ZuIhLxX3AsEvHT2bmJioo7LUo1d/X1j1gdQSk14ejRv+fEicdYvPjzVFXdY3Wcy5Y2PfRA4OicjqcyY0xKrzujVDbo79/O4cObKC+/k9raB62OExdpU9Cnu02Rbrcvzp59mT/+cR6Dg89YHUWprFZc/EYWLfocK1c+llZjzWcSU0EXkQ0i0i4iHSLywAzn3SoiRkSibo90OerqHsJmc19wzGZzU1f3ULybSiiXayHh8Gl9MKqURUZGjhAO+3E4Cqmr+0razGOJxaz30GV8XOBm4EagG9gpItuNMW1TzitgfIPoFxIR9Nx98s7OTQQCR3G5FlFX91Ba3T8HyMkpwems1oKuVBL5fFvP1w6w4/E08trX7rY6VtzF8lB0HdBhjOkEEJFtwC1A25Tz/gfwNeCzcU04SUXFnWlXwKMZ3+xCJxcplQznRsi9OqgihN+/H59va0bUk8liueWyADg26XX3xLHzRGQtsNAY88uZLiQiG0WkRURa+vr65hw2U3g8Dfj9bWk1y1WpdBVthJwxo3R2brIoUeLE0kOP9rTg/OpSMr6rxDeBD8x2IWPMFmALQFNTU9auUFVS8jZstlwikVHsdo/VcZTKSJFICJvNkVEj5GYTSw+9G1g46XUN4J30ugBoBJ4VkSPA64DtiXgwmilKSt5CXd1XtJgrlQB+fwcdHZ+lubmGQKAnY0bIxSKWgr4TWC4iS0TECdwObD/3pjHmtDFmvjGm1hhTCzwP3GyMaUlI4gwRCp0hEOixOoZSGcGYMP39v+Dllzfwpz8t5/jxb1Fc/AYikZGMGSEXi1lvuRhjQiJyP/AMYAe+b4xpFZEHgRZjzPaZr6CiaWm5ksLCdaxe/WOroyiVtowxiAiBQDd7974Lp7OS2tq/o6rqw+cX2MrLqwPSf4RcLMSqzRaamppMS0v2duJfeeVmRkcP89rXvmJ1FKXSijGGoaHn8Xq/Qzg8TGPj08D4DmYFBeuw2XIsTphYIvKiMSbqLe20Wcsl03g8jQwO/opIZAybzWl1HKVSXjg8jM/3Y7ze73D27G7s9kIqKz+IMRFEbBQVXWd1RMtpQbeIx9OIMSFGRg7i8TRYHUeplHf8+GY6O/8Gj2cNK1Y8THn5nWm5CUUiaUG3yLkiPjy8Vwu6UlNEIiEGBn6J17uZysoPUVFxO5WV91BYeC1FRddlzNor8aYF3SJu90qWL/82BQWvtTqKUiljbMyH1/soPT2PEAh043LVAOMT8JzO+Tid11sbMMVpQbeIzeZiwYL7rI6hVErZs2cDZ8++xLx5N7Js2T9RWvqOtNuo2Ur6SVlodLSb4eFXKC29yeooSiVdKHSW3t6t+HxPsGbNr3A48lm27B9xOstxu+utjpeWtKBbqKfne3R1PcgNNwyn/dZXSkUzeZXDc+O/8/PX4vV+lxMnfkQ4fIb8/LUEAt04HCspLr7B6shpTQu6hTyeRsDg9++joOAqq+MoFVdTVzkMBLpob/8wkcgoIk7Ky99LdfV9FBZeow8540QLuoXGC/r4SBct6CrTRN8HeBSHo5R169pwOsstSpa50mYLukyUl7cMEaeuja4y0nSrGYZCg1rME0QLuoVsNgdu90rdvUhlJKezOurxTFzlMFXoLReL1dc/Rk5OqdUxlIqrSCR40QqHkLmrHKYK7aFbrLCwiby8JVbHUCquzpz5E4FAF9XV9+FyLQYEl2sx9fVbMnKVw1ShPXSLjY3109v7Y0pKbsLtXmZ1HKXioqjoOq65poPc3IXAt62OkzW0h26xcHiIjo6Pc+rUs1ZHUeqyDQ/vp7f3SYCJYq6SSXvoFsvNrcVmc+uDUZX2QqGztLa+m2BwgJKSm3A4Cq2OlHW0oFtMxIbH06AFXaU1YwwHDnwEv7+d17zmN1rMLaK3XFKAx9OI369j0VX6On782/T2bmPJki8zb96fWR0na8VU0EVkg4i0i0iHiDwQ5f17ReQVEXlJRP4oIqvjHzVzeTyNjI2dIBg8aXWUS+LzbaW5uZZnn7XR3FyLz7fV6kgqiUZGDnPo0KcpLX0nixb9jdVxstqse4qKiB04ANwIdAM7gTuMMW2Tzik0xgxN/Hwz8N+NMRtmum627yk6WSg0BNjScveVqet1wPhYYx2ell16e3/KvHlvISdnntVRMt5Me4rG0kNfB3QYYzqNMWPANuCWySecK+YTPIA1O0+nKYejMC2LOUy3Xoefzs5NFiVSyWJMmOHh/QCUl9+mxTwFxFLQFwDHJr3unjh2ARG5T0QOAV8DPh6feNmjq+shjh//jtUx5my69TqmO64yx+HDX6Cl5Ur8/g6ro6gJsRT0aOtaXtQDN8ZsNsYsBf4G+HzUC4lsFJEWEWnp6+ubW9IMNzj4a3p7f2x1jDmbbl0OXa8js/X3/4KjR79CZeVdOiEuhcRS0LuByTMEagDvDOdvA94V7Q1jzBZjTJMxpqmsrCz2lFnA42lkeHgvsz3TSDU1NZ+OctTOkiVfTnoWlRwjI53s23cX+flXsWzZP1kdR00SS0HfCSwXkSUi4gRuB7ZPPkFElk96+XbgYPwiZge3u4FQ6BRjYz1WR5mTUGgAAKdzASA4HPOAMCMj+jU8E4XDI7S2vgcRoaHhKez2XKsjqUlmnVhkjAmJyP3AM4Ad+L4xplVEHgRajDHbgftF5C1AEDgJ3J3I0Jlo8mYXLlf0ZUdTTSQSpKfnUUpK3sYVV/wbMD7BZP/+D9LV9XcUFFzN/PnvtDiliicRB/PmvYXi4jfqonIpKKaZosaYHcCOKce+MOnnT8Q5V9bxeBpwOOYRDA5YHSVmo6OHEXFSXX3v+WMiwooV32V0tPOi0S8qvUUiIWy2HJYu/V9WR1HTmHUceqLoOPSLGWPSbm9FY8KAIGKbcjz9/lnU9M6c2UVr63tpbPwX8vNfY3WcrHa549BVkqRTAQyFThOJjCFiv6iYw6v/LCdO/Ih9++5Ku4e96lXB4CCtre/BmMC0uxCp1KAFPYWcOPEEu3atx5iI1VFm1dX1ZZ5/fgnh8MiM542N9eLzPcGxY/o1PR0ZE2Hfvr8gEDjO6tU/xenU0WmpTAt6ColERhgaep7R0S6ro8woHB6lp+cHFBaux27Pm/HchQs/S1nZe+ns/ByDg79JUkIVL0eP/j2Dg//GsmXfpKjodVbHUbPQgp5CJo90SWV9fU8RCg2wYMFfznquiFBf/xgez2ra2m5nZORI4gOquDAmwunT/0V5+Z9TXf3frY6jYqAFPYV4PA1A6hd0r/dh8vKWU1z8ppjOdzjyaWj4GRBhcPDfEhtOxY2IjTVrtlNf/1haPd/JZlrQU4jDUYjLtZDh4dRdG314uI2hoeeorv5o1Ieh03G7l7FuXTsLFtyXwHQqHiKRMQ4cuJ9A4Dgidp08lEa0oKeY0tJ3pvRejG73Kq688j+orPzgnH/X6SwH4PTpZnp6fhjnZCpeDh36DF7vZoaGXrA6ipoj3YIuxaxYsdnqCDMSEYqLX39Z1zh27OsMDGwnL28ZxcXXxymZigef7585fvzb1NR8mrKyd1sdR82R9tBTVCqO2z5x4gkOHvwY4fDoZV2nvv4xcnOX0NZ2G4HATOu8qWQaHm6lvf0jFBVdT13d/7Q6jroEWtBTjN9/kOeeK6e//2mro1zAGEN39zc4deo/sdlcl3WtnJxiGht/Rih0htbWW4lExuKUUl2Ozs4HsNsLWL36J9hsOVbHUZdAC3qKcbkWEAz2p9xIlzNndnL27G6qq++Ny4gHj6eBlSt/wNBQMz09j8Yhobpcq1Y9wRVX/BqXq8rqKOoS6T30FGO3u8nLW5pyBd3r/S52ez4VFe+P2zXLy2/D4XiGefPeErdrqrkbHPwtRUXX43AUUVBwpdVx1GXQHnoKOrfZRaoIBk/S27uN8vI7cTgK4nrtkpK3ImJjdLSbs2dfjuu11exOnfojr7xyE0eOfNHqKCoOtKCnILe7Ab//IJFIwOoowPiSBBUV749pZuilMMbQ2norr7zyTsbGehPShrrY2JiPtrb3kptby+LFf2t1HBUHWtBTUEnJjdTUfHLWha+SxeWqpr7+0YQtmzq+hvp3CAb7aGt7H5FIKCHtqFdFIiHa2m4nFDpFQ8O/4HAUWR1JxYEW9BRUXPwGli37Ojk5xVZHYXi4laGhPyV8GGVBwVWsWPEIp049S2fnAwltS0FX199x6tSzrFjxCPn5V1gdR8WJPhRNUeHwCKHQaVyuSktzHDnyICdP/ob1670JnwJeWfkXnDmzk+7u/01R0fWUlUXda1zFQUXFXdhsHior77I6ioojLegpateua8jNrWXNmu2zn5wgY2M++vufZsGCjyVtPY+lS7+BwzGPefNiW/hLzU0weAqHowi3ewWLF+s3oUwT0y0XEdkgIu0i0iEiF/1XICKfFpE2EdkjIv8uIovjHzW7uN2rLR/p0tPzGMaEqK7+aNLatNlyWLLkQRyOovPfUlR8hMN+XnrpDRw8+DGro6gEmbWgi4gd2AzcBKwG7hCR1VNO2w00GWOuAJ4CvhbvoNnG42lkdPQwodBZS9o3JozXu4Xi4j/D7a63pP2XX/4z2trumNi3VF0OYwwHDtzL8PArzJ//TqvjqASJpYe+DugwxnQaY8aAbcAtk08wxvzeGHNui/fngZr4xsw+5za78Pv3WdK+33+AcPg01dX3WtK+iJ2Kir9gcPBXHDnyJUsyZJKeni34fI9TW/slSkr+P6vjqASJpaAvAI5Net09cWw6HwJ+Fe0NEdkoIi0i0tLX1xd7yixk9e5FHs8q1q8/zvz51j2YrK6+l8rKD9LV9WX6+39uWY50NzS0k4MHP05JyU0sXvx5q+OoBIrloWi0hTuijmETkfcDTcAbor1vjNkCbAFoampKveUEU0he3hKWLv0GhYXrk952ODyKzebCbncnve3JRITly7/D8PAr7Nt3F1dfvdOS2z/pyOfbSmfnJgKBo+TklOF0VrFq1eNz2pREpZ9Y/u12A5N3XKgBLlrzVETeAmwCbjbGpMYUxzQmYmfhwk/h8axMettdXV9m586Gy14mNx7s9lwaGv6FgoKr0WkTsfH5ttLevpFAoAswBIO9BIN9DA7+2upoKsFi+RuyE1guIktExAncDlwwlk5E1gKPMF7Mde52nAQCJxgcfCapbUYiQXp6vkde3tKU2XosN3cRV175e9zu5RhjUnKt+FTS2bmJSMR/wbFIxE9n5yaLEqlkmbWgG2NCwP3AM8A+4CfGmFYReVBEbp447X8B+cBPReQlEbFu8HQG8fn+D3v2bCAYPJm0Nvv7f04w6LPsYehMIpEx9u37c44e/arVUVJaIHB0TsdV5ohpYpExZgewY8qxL0z6Wdc/TYBXH4y2Jm2rNq/3YVyuxZSUbEhKe3MhMr7pwuHDmygouIqSkrdanCj19PdP35dyuRYlMYmygt6UTGHJHuni9x/g1Kl/p7p6I+PTD1KLiFBf/z08ngba2u5gZOSw1ZFSyuhoF62t78HlWozNlnfBezabm7q6hyxKppJFC3oKc7kWYrcX4Pe3JqW93NwlrF79E6qqPpSU9i6F3e6hsfFnQITW1ncTDvtn/Z1MFw4PA5Cbu5g1a3awbt0+6usfxeVaDAgu12Lq67dQUXGntUFVwulaLilMRJK62YXNlkN5+fGZ8V8AAA33SURBVG1Jaety5OUtZdWqrezffzd+fzsFBWutjmSZkyf/nba2O6mv/x7z57+DkpIbAaiouFMLeBbSgp7ili37x7jvEhRNX9+/Mjz8CosW/fVlbwKdDKWlb+OaazqT8tmkImMidHU9xJEjX8TtXkleXp3VkVQK0FsuKa6wsCkpk2mOHfsaPt8TjI9MTQ8ORwHGGLq6/ienTv2H1XGSZmysnz173saRI1+gvPzPueqqP+HxTF1eSWUjLegpLhQ6jdf7CMPD+xPWxtmzLzM01Ex19b2IRJsYnLrC4WFOnPgBra3vJRA4bnWcpBgY+AWnTv2eFSseZtWqx3E48q2OpFKEFvQUF4mMcuDAvZw8mbgJRl7vw9hsuVRW3p2wNhLF4cinsfFnRCJ+du16A83Ni3n2WRvNzbX4fFutjhc3xhj8/nYAKis/wLp1+6iu/mja/Q9YJZYW9BSXk1NOTs78hD0YDYXO4PM9QVnZ+8jJKUlIG4nm8aymsvIeAoFDE5NnDIFAF+3tGzOiqIdCp2ltvZUXX2xidPQoIqL3zFVUWtBT3KsjXRIzdDEUGqS4+E0sWPCXCbl+skRbjTETprufOfMSLS1X09//cxYv/iIu18LZf0llLR3lkgbc7gZ8vv+DMSbuX7HHxy6n/0oN00937yIcHsZu9yQ50eXr6XmMAwfuIyenlCuvfDZps4VV+tIeehrweBoJh88QCHTH9bojI52MjHTG9ZpWmWlae3PzQg4deoDR0fh+fok2NLST4uIbaGrarcVcxUSsWrmuqanJtLS0WNJ2ugmFhjAmQk5OcVyvu2/f3fT3/5xrrz2RMisrXqpzS8ZOXmXQZnOzcOFf4fe30tf3NCI2Ghp+xvz577Aw6cz8/nYikQD5+VcQiYwhYk/JZRiUdUTkRWNMU7T39JZLGnA4CuN+zWBwkN7eJ6mquiftizlwflbkuU0dXK5F1NU9dP74yMgRvN7vUFx8AwADA78mEhlm/vx3pUzB7O19kvb2D+PxNLJ27X9hs6XPnACVGrSgp4ljx/4BETs1NfHZsf3EiR9iTCAll8m9VDNNd8/Lq2Xp0lf3Lvd6v8vAwHZyc2tZsODjVFV9KCH/44xFJBKgo+MzeL2bKSy8joaGJ3U4orokeg89TQwO/ooTJ34Yl2sZY/B6H6aw8Fry86+IyzXTTWPj0zQ0PI3LtZBDhz5Nc3MN3d3fSnqOsbE+du++Aa93MzU1n+HKK3+PyzXTlr1KTU8LeprweBrx+9swJnzZ1/L72xgd7cqo3vlcidgpK/tvrF37n1x9dQulpTfjcJQC4+O+T536Y1J2RnI4inE6K2hoeJply76OzZaT8DZV5tJbLmnC42kkEhllZKQTt3v5ZV6rgfXrj2G3W3OLIdUUFFzN6tVPnH/d0/MDDh36FAUFTdTUfIqystviWmgjkRDHjn2VqqqNOJ1lrFnzi7hdW2U37aGnCY+nAbj8zS6MiQDgdJZnxMPQRKiu3sjy5d8lFDrDvn138vzzSzh69KvnP7vLEQj08PLLb+bw4c/T2/tkHNIq9aqYCrqIbBCRdhHpEJEHorz/ehHZJSIhEbk1/jGV270au72AYHDgsq7T1fUVdu9+PeHwaJySZR673c2CBfeybl0ba9b8Erd7JYODv0Fk/K/L2Nil7YN+8uTvaGm5kjNnWli58nFqau6PZ2ylZr/lIuNjujYDNwLdwE4R2W6MaZt02lHgA8BnExFSjS9Cdf31py9r9IMxYXp6tuB212vvPAYiNkpL305p6dsJh0cACASO8/zzdZSUvJWamk9RXPymmP6d9PY+SVvbn+N2r+DKK393/huXUvEUSw99HdBhjOk0xowB24BbJp9gjDlijNkDXP53UjWtyx3KNjDwKwKBY1n9MPRS2e3je3TabHksWvQ5hoZe4OWX30xLy5X09PyQSCQw4+8XF7+ZBQvu56qrdmoxVwkTS0FfAByb9Lp74phKsr6+p9m16zoikbFL+n2v92GczipKS2+Oc7LskZNTwpIlX+J1rztKff1jQIT29g8xNnYCgBMnttLcXMuzz9p47rkqXnxxPZFIEKdzPsuXf0vXLlcJFUtBj9YtvKTxXCKyUURaRKSlr6/vUi6R1SKREYaG/ouRkYNz/t2RkSMMDu6gqurDOjQuDuz2XKqq7qGpaQ9NTbvJzV2Mzze+z2kg0AUYgsETnDnzAsePb7Y6rsoSsRT0bmDymp01gPdSGjPGbDHGNBljmsrKyi7lElnN42kELm2kS05OKcuX/xNVVR+Jd6ysJiLnJ2d1dv4tMHWegKG7+x+Snktlp1gK+k5guYgskfENJ28H0n+91TSUl1cP2C+poDscBSxYcB+5ubqedqIEAsemOR59aV+l4m3Wgm6MCQH3A88A+4CfGGNaReRBEbkZQEReKyLdwG3AIyKSmN0YspzdnovbvXzOBX1w8Ld4vVuIRIIJSqZg+iV8Z1raV6l4immmqDFmB7BjyrEvTPp5J+O3YlSClZRsmPP0/6NHv8Lo6GGqqj6coFQKoK7uoahL+NbVPWRhKpVNdOp/mlm27JtzOn94eD+nTv2eJUv+/vzEGJUYsy3hq1SiaUFPU7FuR9fT8wgiOVRV3ZOEVGqmJXyVSjTtsqWZ0dFumpsX4vM9Puu54fAIJ078kLKy9+B0lichnVLKSlrQ04zTWcnYWG9MD0bHxry43St1ZqhSWUJvuaQZm82B270qpoKel7eUq65qTkIqpVQq0B56GvJ4GhkennlkaCDQc9krMyql0osW9DTk8TQSCBwlFBqa9pwjR77ECy+smHXRKKVU5tCCnoaKi9/AggUfIxKJvqZ5KDSEz7eV+fPfhc3mSnI6pZRV9B56GioqWk9R0fpp3/f5niASGdaHoUplGe2hp6lIJBh15xxjDF7vw+TnX0VBQZMFyZRSVtGCnqZeeukNtLXdcdHx4eG9DA+/QnX1vZe9IYZSKr3oLZc05XavZGBgx0XH8/PXsG5dOy6X7kGiVLbRHnqa8ngaCQZ9jI1dvFGI270Cu91jQSqllJW0oKepVze7eHU8enf3t9i79z06VFGpLKUFPU2dK+h+/3hBNybC8ePfJhjs1aGKSmUpLehpyumsYunSr1NUdAMAJ0/+jpGRDh2qqFQW04eiaUpEWLjwM+dfe73fJSdnPmVlt1qYSillJe2hp7FgcIDBwd8SCHjp7/85lZUf1NstSmUxLehprLd3G3v23Egw2M/ixZuorv6o1ZGUUhaKqaCLyAYRaReRDhF5IMr7LhF5cuL9F0SkNt5B1cUCgR4AWlqu5MSJHzE09LzFiZRSVpq1oIuIHdgM3ASsBu4QkdVTTvsQcNIYswz4JvDVeAdVF/L5ttLd/Y2JV4ZAoIv29o34fFstzaWUsk4sPfR1QIcxptMYMwZsA26Zcs4twI8mfn4KeLPovPOE6uzcRCQycsGxSMRPZ+cmixIppawWS0FfAByb9Lp74ljUc4wxIeA0UDr1QiKyUURaRKSlr+/iGY4qdoHA0TkdV0plvlgKerSetrmEczDGbDHGNBljmsrKymLJp6bhci2a03GlVOaLpaB3Awsnva4BvNOdIyIOoAgYjEdAFV1d3UPYbO4LjtlsburqHrIokVLKarEU9J3AchFZIiJO4HZg+5RztgN3T/x8K/A7Y8xFPXQVPxUVd1JfvwWXazEguFyLqa/fQkXFnVZHU0pZZNaZosaYkIjcDzwD2IHvG2NaReRBoMUYsx14DHhcRDoY75nfnsjQalxFxZ1awJVS58U09d8YswPYMeXYFyb9PArcFt9oSiml5kJniiqlVIbQgq6UUhlCC7pSSmUILehKKZUhxKrRhSLSB3Rd4q/PB/rjGCfd6edxIf08XqWfxYUy4fNYbIyJOjPTsoJ+OUSkxRjTZHWOVKGfx4X083iVfhYXyvTPQ2+5KKVUhtCCrpRSGSJdC/oWqwOkGP08LqSfx6v0s7hQRn8eaXkPXSml1MXStYeulFJqCi3oSimVIdKuoM+2YXW2EJGFIvJ7EdknIq0i8gmrM6UCEbGLyG4R+aXVWawmIsUi8pSI7J/472S91ZmsIiKfmvh7sldEfiwiuVZnSoS0KugxblidLULAZ4wxq4DXAfdl8Wcx2SeAfVaHSBHfAn5tjFkJvIYs/VxEZAHwcaDJGNPI+DLgGbnEd1oVdGLbsDorGGN6jDG7Jn4+w/hf1ql7vWYVEakB3g58z+osVhORQuD1jO9VgDFmzBhzytpUlnIAeRM7qrm5eNe1jJBuBT2WDauzjojUAmuBF6xNYrl/AP4aiFgdJAXUAX3ADyZuQX1PRDxWh7KCMeY48HXgKNADnDbG/F9rUyVGuhX0mDajziYikg/8C/BJY8yQ1XmsIiLvAHqNMS9anSVFOICrgO8aY9YCw0BWPnMSkXmMf5NfAlQDHhF5v7WpEiPdCnosG1ZnDRHJYbyYbzXGPG11HotdB9wsIkcYvxX3ZyLyhLWRLNUNdBtjzn1re4rxAp+N3gIcNsb0GWOCwNPAtRZnSoh0K+ixbFidFUREGL8/us8Y8w2r81jNGPM5Y0yNMaaW8f8ufmeMycheWCyMMSeAYyJSP3HozUCbhZGsdBR4nYi4J/7evJkMfUAc056iqWK6DastjmWV64C7gFdE5KWJY387sf+rUgAfA7ZOdH46gQ9anMcSxpgXROQpYBfjo8N2k6FLAOjUf6WUyhDpdstFKaXUNLSgK6VUhtCCrpRSGUILulJKZQgt6EoplSG0oCulVIbQgq6UUhni/wG1yo4XNfKCHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,color = 'y',marker = 'o',linestyle = '--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26f13801a20>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W2woLbXuc9Ohhd9T9xqhzYSxebO22O3WyIZe4zGNJRZMJXUQKgHHACUB/4BwR2aq+REQ6YgtEv5LuIME+FpWW2pi5iD2Wltr2ONlpJyuJ8oTuXPbUVsi1aQN9+sDAgaEjyoxUrtAHAAtUdaGqbgCmAMnW6/gFcAewPo3xbWHYMPvtWl1tj3FL5rUSCZ9c5Fy21K+Qq6qyhltxqpBLVSoJvRuwtM7zippt/09EDgB6qOpfGzuQiIwQkXIRKa+srGx2sLmiuBjmzo3XLFfn4mr0aBtiqWv9etuea1LpVJDsdsH/1zqKSBvgbuD8pg6kqqVAKVjZYmoh5p4TT7Sm+evXw/bbh47GudxUVWW9WHKpQq4pqVyhVwA96jzvDiyv87wjkABeEJHFwMHAjEzcGM0Vxx5rN2M8mTuXfgsWwDXXQPfutt5wrlTIpSKVhF4G9BGR3iLSDhgKzKh9UVW/UNUuqlqkqkXAy8AQVU191lAe+vJL+8fmnGu9TZvgiSdg8GC76XnvvXDEETaVP1cq5FLR5JCLqlaJyChgFlAA/F5V54jIGKBcVWc0fgSXzP77W+vfP/0pdCTOxZeqVb1VVMBpp8G3vgU//7ktTFHbYGuPPexx9GgbZunZ05J5XIsqGhObqf+5ZsgQWLQI3nkndCTOxYsqvPwy/M//wNdfw/Tptv2ll+wiaZttwsaXaTkx9T/XJBJWOrVhQ+hInIuHr7+GBx6A73wHDj3Upu737Lm5jcbAgbmfzJviCT2QRMLuwn/wQehInIuHcePg4ovt/8348bBsGdxzj00WcsbfikB8sQvnGlZVBY89BscdZ0vBAVx4IfzrX9YPaeTIeK4olGkxWzEvd+yzD/zud/Dd74aOxLno+OQTuP9+W1C9osJKD2sn4HXpYotQuIZ5Qg9k221tZXHn3GaDB8Obb9qV+W9/CyefHL+FmkPyIZeAKirgySdDR+FcGF99ZVfigwbZ9wC/+Y0VC/z971aG6Mm8eTyhB/TAA3DSSfFfx9C5htTtclhUZM/nzoXLL7c68UsuseqVigrbf9Ag2HvvkBHHm//+CyiRsJraefPgwANDR+NcetV2OaxtjLVkiU34Wb/eFno/6ywbdjzooNxZYCI0T+gBJRL2+O67ntBd7mmoy+HOO9tV+i67hIkrl/mQS0B77WVXKt4b3eWihroZrlrlyTxTPKEH1LatlS96LbrLRbW9VOrLxS6HUeFDLoFNnGgfQZ3LJRs3bt3hEHK3y2FU+BV6YCUl0Lt36CicS69XX7WboJddFv91gOPEr9AD++wza6F7wgk2pu5cLhg40Baa6NHDZkS77PAr9MDWrIErroAXXggdiXOt9957MHWqfd+jR+P7uvTzK/TAiopsXNFvjLq4++or+P73YeVK+8TZqVPoiPKPJ/TA2rSxzoue0F2cqVpr2/nz4emnPZmH4kMuEZBIeC26i7ff/c7a3N56Kxx9dOho8ldKCV1EBovIfBFZICLXJXn9EhF5R0TeFJEXRaR/+kPNXYkEfPwxrF4dOpKWSdavw+WPRYvgqqvglFPgZz8LHU1+a3JNUREpAN4HjgMqgDLgHFWdW2efTqq6pub7IcCPVHVwY8fN9zVF61qzxpJhHBv21+/XAXZPwMvT8sujj8Kxx8KOO4aOJPe1dk3RAcACVV2oqhuAKcCpdXeoTeY1tgfCrDwdU506xTOZQ/J+HWvX2naX2zZtsqoWgDPP9GQeBakk9G7A0jrPK2q2bUFELhORD4E7gCvSE17+GDvWVjGPm4b6dTS03eWOm26C/fe3enMXDakk9GSNLbe6AlfVcaq6J/Az4MakBxIZISLlIlJeWVnZvEhz3FNP2QSjuGmoL4f368htTzwBt90G553nE+KiJJWEXgHUnSLQHVjeyP5TgNOSvaCqpapaoqolXbt2TT3KPJBIWOliE7c0Iueqq7beVlBg1Q4uNy1caIn8wANtmTgXHakk9DKgj4j0FpF2wFBgRt0dRKRPnacnAR+kL8T8UFwMn38OK1aEjqR5Vq60x27drF/Hjjva2Kp/DM9N69bB6afb3/W0abDddqEjcnU1mdBVtQoYBcwC5gGPqOocERlTU9ECMEpE5ojIm8BVwPCMRZyj6i52ERcbN9oK7SeeaEuIVVdbgh8+HH7+c/tY7nJL27ZWzfLQQ95ULopSmimqqjOBmfW23VTn+yvTHFfeKS62q9vaK944WLTIFui45JLN20TgvvvsY3n96hcXb1VVsM028N//HToS1xCf+h8RXbtaMo/T2op9+8KHH24dc/v28I9/xOvP4hr3+uu2Buif/wz77Rc6GtcQn/ofIXFKgF98ARs22A3QNkn+FdX+Wf74R7uBFrebvW6zVats3PybbxpehchFgyf0CHnoITjkEBuLjrpbb7Ux1HXrGt/v00/tz+Uf0+Opuhp+8ANYtsxmg3pxWrR5Qo+Qdevg5ZdtpZcoW78eHnzQfvm0b9/4vtdcYx/Vr7/euvC5ePnlL+Fvf4O774aDDw4djWuKJ/QIiUuly7RpNt5/6aVN7yti66b27w9Dh8LixRkPz6VJdTX8+9/wn/8JP/pR6GhcKjyhR0hxsT1GPaGPHw99+sBRR6W2f4cO8Je/WIL4298yG5tLnzZtYMYM+4Ucp/s7+cwTeoR06mTLdkW5N/rcufDSSzByZPKboQ3Zay9b/OCyyzIXm0uPDRtg1CgbNy8o8MlDceIJPWJOOSXaazH262cliRdc0Pyf3WUXe5w9G/7wh7SG5dLo6qth3Dh45ZXQkbjm8jr0iBk3LnQEjROBww9v3THuvNM+yu+1Fxx2WHricunx8MO2+tBVV9n6oC5e/Ao9oqJYt/3QQ3D55Vbl0hoTJ1rJ45lnwvLG2ry5rJozx9YFPeww+NWvQkfjWsITesR88IENTUyfHjqSLanCXXfBP/8J227bumN17mw3Sb/8Es44w8ZsXXjXXQcdO8Ijj9gUfxc/ntAjpls3+Oyz6FW6lJXBG29Y35Z0VDwUF1st++zZ1uDLhffQQ9aXf7fdQkfiWsoTesQUFsKee0Yvod93n5Ufnntu+o555pkwa1Zq9ewuc555xobRdtjBViBy8eUJPYJqF7uIitWrYcoUW/S5Y8f0Hvv44638saIC3norvcd2TXvxRTjhBLj55tCRuHTwhB5BxcU2lv7NN6EjMevW2ZV5pq6kVW0s/ZRTrPeLy45PPrG2DEVFcMMNoaNx6eBlixF03HH2EXjdutbfgEyH3XfP7Di3iC2QPXAgnH229Xxp6/8yM6qqyloxfP65jZvvsEPoiFw6+BV6BB1xhNVqd+4cOhIrZXv11cyXUR54IEyYAC+8YNUWLrN+/nN7rydMgH33DR2NSxe/Doqodeus5/i3vhU2jjFj7Ip5+fLMTwH/wQ+smubXv7Za6NOSLjXu0uG882D77e3R5Q5P6BF10EE2tjljRpO7Zswnn1g9/OWXZ6+fx1132VJ8qTb+cs3z+ec2vNK3r38SykUpDbmIyGARmS8iC0Rkq38GInKViMwVkbdF5FkR6ZX+UPNL//7hK10mTrSx1pEjs3fObbaxTwU77LD5U4pLj7VrbTjv8stDR+IypcmELiIFwDjgBKA/cI6I9K+32xtAiaruC0wD7kh3oPkmkbBFmL/6Ksz5N22C0lI4+mjYe+8w5z/6aDjnHPvetY6qTQp75x2rJnK5KZUr9AHAAlVdqKobgCnAqXV3UNXnVbV2jfeXge7pDTP/1C52MW9emPO//75dHV9ySZjzFxTYmPqTT8Itt4SJIZeUlsKkSfZe/sd/hI7GZUoqCb0bsLTO84qabQ35IfBkshdEZISIlItIeWVlZepR5qHQqxf162f9sEPemLzkEmvTe+ut8Pjj4eKIu7IyuOIKm0B0442ho3GZlEpCT9a5I2kRm4icC5QASZcEVtVSVS1R1ZKuvtpso3r3thuEhxyS/XOvX28f0QsLwzZpqq1PLymxaoz588PFEjeTJ9tN9TZt4OSTrT/LpEnNW5TExU8qf70VQN0lF7oDWzU9FZFjgdHAEFWNyBzH+CoogJ/8BPbZJ/vnvvVWm63a2ja56bDddvDnP8N3vuPJKFWTJ8OIEbbYuKrNvq2stAlELrel8l+kDOgjIr1FpB0wFNiimE5EDgAmYMncJ2+nyccfW/OqbNq4ER54wBqERWXpsZ494fnnbR1T1Wj2io+S0aOtoqWutWttu8ttTSZ0Va0CRgGzgHnAI6o6R0TGiMiQmt3+G+gAPCoib4pIwOrp3PG//wuDB1tzrGx5/HGrPw91M7QxGzbYCvS33x46kmj76KPmbXe5I6WJRao6E5hZb9tNdb4/Ns1xOTbfGJ0zJ3tLtY0fD7162S+SqKkdzx892loFHH982HiiqLGJaD17Zi8OF4aPSkZYtitd3n8fnn3Wxl8LCrJzzuYQseGg4mKrT1+0KHRE0bJkCZx+uv1Cbt9+y9cKC2Hs2DBxuezxhB5hPXpY//E5c7Jzvt69bfmxH/4wO+drie23t+XrqqttEeP6Y8X56Ouv7bFXL5g50+Yu3H+/PRexx9JS62fvcpsn9AgTye5iF9tsY6sI7bprds7XUnvuaZUcFRVeyvjss/Z+/PWv9vy44+xm9rBhsHix/eJbvNiTeb7w5lwR95vfpH+VoGQee8ymhV97bTR6sDflxBNh4cLsvDdRVF1tQyg332ylrXvsEToiFwV+hR5xJSXZ6aVyxx22SHC7dpk/V7p07GgljL/6FfzjH6GjyZ7PPrNfaDfdZFU/r75qzdyc84QecV98YYsQvPde5s7x1lswe7aVKkqyecER9vXX8OCDtpTasmWho8mOJ56wuvzx4232Z4cOoSNyUeEJPeLWr7dEm8kJRuPH27jr8OGZO0emdOhgN0lrW8P26mUzSouKbJw9V6huvl9w/vl243PkyPj9AnaZ5Qk94nbZBbp0ydyN0S+/tKGWs8+GnXbKzDkyrX9/uPBC+PBDmzyjaiV8I0bkRlL/4gtbRLukxP58Ij5m7pLzhB5xtZUumSpdXLXKVge69NLMHD9bknVjzIXp7m++aX1sHn/cboD26NH0z7j85Qk9BoqL7Qo9Ez1MevWy2YUHHZT+Y2dTQ9PalyzZXKcdNxMnwsEH28pNL7wA11zjQyyucZ7QYyCRsKGRior0HnfhQvvKBY1Na+/Rw9bPTPf7l2llZTBoELzxRvZaP7h4Ew3Uuq6kpETLy8uDnDtu1qyxuuPOndN73OHD7aP8xx9Hp7NiS9W2jK07c7SwEH76Uxuumj7dbpb+5S/WHzyq5s+Hb76Bffe1ZmQFBdFsw+DCEZHXVLUk2Ws+sSgGOnVK/zFXrYKpU+1mYtyTOWyeCTl6tA2/9OxpE29qty9ebItlDBpkz596yoZiTjstOglz6lS46CL7RPbvf8drToCLBh9yiYl77oHf/jZ9x/vDH+xKMIptcluqsenuRUU2eWqHHez5ffdZ5chee8Hdd9unoFC++QZGjYKhQ2G//WDaNB8rdy3jCT0mnnzSknA6qFrt+aGH2kf7fDR9un316AFXXQXdu8O992Y/jspK+9QwbhxcfbVNGOrW2Iq9zjXCE3pMJBIwdy5s2tT6Y82da9UfuXR13lwFBfC978E//wnl5TBkCOy8s732xRfw4ovZWRmpc2drhjZ9Otx5Z9g1XF38eUKPiUTCZo2moyqluBiWLrXOis7qvB96CM49154/+KBdNQ8YAA8/bMvypVNVlY3vV1ZaAn/iCfvl4lxreUKPieJie2ztjNHqanvcZZfcuBmaCSNG2Bj7l1/aOHzv3rbsXe171xorVsAxx8CNN9pNUOfSKaWELiKDRWS+iCwQkeuSvH64iLwuIlUickb6w3T9+1t3wZUrW3ec226Dww+3q32XXGGhDUfNnWt9xvfZB55+2soeAT5t4TLozz0H++9vQzyTJtmNUOfSqcmELiIFwDjgBKA/cI6I1G/W+RFwPvBwugN0pkMHG9u96KKWH2PTJlu5Zttt/eo8FW3awEknwTPP2LAIWEfHHj3glFMsQac6zj51qi0+sdNO1u62dnjHuXRK5Qp9ALBAVReq6gZgCnBq3R1UdbGqvg2k4UOpa0hrS9mefNLGzvP5ZmhL1a7R2azPyB4AAAejSURBVL49XH89vPKKDZ3sv//mEtDGHHOMXZGXlW0ePnMu3VJJ6N2ApXWeV9Rsc1k2fToMHGgzCFti/HjYbTer6HAts9NOcMstNnlp4kQbV//hD222LdiM1aIiu7rfbTc45BC7qdqli5VFeu9yl0mpJPRk14UtKugSkREiUi4i5ZWVlS05RF5bt85mEH7wQfN/dvFiW0D4oou8NC4dttvOZtm+/bb1WunVy5L58OFWEqpqSf6VV6zG3LlsSCWhVwB1m3Z2B5a35GSqWqqqJapa0rVr15YcIq8lEvbYkkqXnXe2maYXX5zemPKdyObJWTfcsPU8AVWb5etcNqSS0MuAPiLSW0TaAUOBGZkNyyWz9942IaYlCb1jR7jsMu+nnUlLlybf3lBrX+fSrcmErqpVwChgFjAPeERV54jIGBEZAiAi3xWRCuBMYIKIZGg5hvy23XbQp0/zE/ozz1h1S7onyLgtNdTCt7HWvs6lU0rdFlV1JjCz3rab6nxfhg3FuAwbPLj50/9vuw0WLWpdyaNr2tixyVv4jh0bLiaXX7x9bszcfXfz9n/vPWv49Mtfbp4Y4zKjqRa+zmWaJ/SYUk2tLn3CBKtqufDCzMfkLHl7Aneh+DVbzFRU2I3NSZOa3nfdOpv0cvrp1rvFOZfbPKHHzLe+Zb1EUrkxuny59SHxmaHO5QcfcomZtm2hX7/UEvqee8Ls2ZmPyTkXDX6FHkOJhC183JgVK1rfmdE5Fy+e0GMokbAqisbWwbzlFujbt+mmUc653OEJPYaOOAIuv7zhnuZr1lhfkdNOs1a5zrn84GPoMXTIIfbVkIcegq+/9puhzuUbv0KPqY0bk6+co2ptcg88EEpKsh+Xcy4cv0KPqSOOsMUWnn12y+3vvgvvvGO9W1q7IIZzLl48ocfUPvtYf/P6vv1tmD8fuvkSJM7lHR9yialEAj75BJKtE9K3L2y/ffZjcs6F5Qk9pmoXu6hbj37vvTbN30sVnctPntBjqn5Cr66G3/3ObpR6qaJz+ckTekztthvceScMGmTPn3sOFizwUkXn8pnfFI0pEbj66s3P77vPVpY/44xwMTnnwvIr9BhbudKWl1u+HB5/HC64wIdbnMtnntBjbMoUOO44+OwzWyVn5MjQETnnQkopoYvIYBGZLyILROS6JK9vKyJTa15/RUSK0h2o29qKFfa4//7wxz/Cyy+Hjcc5F1aTCV1ECoBxwAlAf+AcEelfb7cfAqtVdS/gbuD2dAfqtjR5Mtx1l32vCkuW2ALFkyeHjcs5F04qV+gDgAWqulBVNwBTgFPr7XMq8Mea76cBx4j4xPNMGj3alpira+1a2+6cy0+pJPRuwNI6zytqtiXdR1WrgC+AnesfSERGiEi5iJRXJpvi6FL20UfN2+6cy32pJPRkV9ragn1Q1VJVLVHVkq5du6YSn2tAz57N2+6cy32pJPQKoEed592B5Q3tIyJtgR2AVekI0CU3diwUFm65rbDQtjvn8lMqCb0M6CMivUWkHTAUmFFvnxnA8JrvzwCeU9WtrtBd+gwbZi1ye/WySUa9etnzYcNCR+acC6XJmaKqWiUio4BZQAHwe1WdIyJjgHJVnQFMBCaJyALsynxoJoN2ZtgwT+DOuc1SmvqvqjOBmfW23VTn+/XAmekNzTnnXHP4TFHnnMsRntCdcy5HeEJ3zrkc4QndOedyhISqLhSRSmBJC3+8C/BZGsOJO38/tuTvx2b+XmwpF96PXqqadGZmsITeGiJSrqoloeOICn8/tuTvx2b+Xmwp198PH3Jxzrkc4QndOedyRFwTemnoACLG348t+fuxmb8XW8rp9yOWY+jOOee2FtcrdOecc/V4QnfOuRwRu4Te1ILV+UJEeojI8yIyT0TmiMiVoWOKAhEpEJE3ROSvoWMJTUQ6i8g0EXmv5t/JIaFjCkVEflLz/+RdEfmTiGwXOqZMiFVCT3HB6nxRBVytqv2Ag4HL8vi9qOtKYF7oICLiXuApVd0H2I88fV9EpBtwBVCiqgmsDXhOtviOVUIntQWr84KqrlDV12u+/xL7z1p/rde8IiLdgZOAB0LHEpqIdAIOx9YqQFU3qOrnYaMKqi3QvmZFtUK2XnUtJ8QtoaeyYHXeEZEi4ADglbCRBHcPcC1QHTqQCNgDqAQerBmCekBEtg8dVAiqugy4E/gIWAF8oap/DxtVZsQtoae0GHU+EZEOwJ+BH6vqmtDxhCIiJwOfquproWOJiLbAgcB9qnoA8DWQl/ecRGRH7JN8b2B3YHsROTdsVJkRt4SeyoLVeUNEtsGS+WRVnR46nsAGAkNEZDE2FHe0iDwUNqSgKoAKVa391DYNS/D56FhgkapWqupGYDpwaOCYMiJuCT2VBavzgogINj46T1XvCh1PaKp6vap2V9Ui7N/Fc6qak1dhqVDVj4GlIrJ3zaZjgLkBQwrpI+BgESms+X9zDDl6gzilNUWjoqEFqwOHFcpA4DzgHRF5s2bbDTXrvzoHcDkwuebiZyFwQeB4glDVV0RkGvA6Vh32BjnaAsCn/jvnXI6I25CLc865BnhCd865HOEJ3TnncoQndOecyxGe0J1zLkd4QnfOuRzhCd0553LE/wE25NBEbGt+MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'b--o') #b代表颜色,--表示线,o表示点的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#曲线\n",
    "x= np.linspace(0,10,100)  #取0到10 ,分100份\n",
    "y = np.sin(x)  #求正弦函数赋值给y\n",
    "plt.plot(x,y)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.cos(x)\n",
    "plt.plot(x,y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#两条曲线整合在一起\n",
    "plt.plot(x,y,label = 'sin(x)')\n",
    "plt.plot(x,y2,label = 'cos(x)')\n",
    "plt.legend()  #在左下角有提示标签\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LL69J5/PcQ7yxYm/j6qhaHB0h3dqFRcH0552LpIt/VWcxYww/e38zYcFBPHHDQIKCGt/L6ZEp/UhpH8nDMzdSWl7pS62bl4oz8MF9EJ0EV/2+8a8PCnI6DoS1gTk/uGDz0l8XbaPgWClPfX0w0RGN72hw2/AujOoVxx/mZrPv8KnG11W1GJocFKQMc7pNrnkFCjbUWuSD9fv5PPcQP57U96J7HbUJC+H31w9gZ9FJnv10hy81bl5WPAuHtsE1/wMR7S5uH23jYfyvYc9y2PhWrUWyC47x8he7ueWSLgzr2v6iwogIj98wgCoDj7fmJkClyUF5jH0Y2nSAeT/x+mZaWl7JH+dvZWBKDN8Y0dWnMGP6xHP1gESeW7aTg8dLfdpXs3CiCJY9Cb2vgt71XICuz5A7IDkTFv4Czpw4b5Mxhl/PyiImMpQfX9XXpzAp7dvwX1f0YM6mAlbvboVNgArQ5KCqRcbCuF86I3W3zj1v06tf7qagpJSHJ/e7qOakmn50VV/KKqr4n8Xbfd5XwFv6eyg/5Qxm81VQEEx+Ak4edM5GzvHZ9kOs2HWYB77Wy5UBhzPG9CChXTi/nZOtF6dbKU0O6j8Gf9O5OP3JH6DK6XJacrqcv3+ygyv6xHNZT3emwugWF8U3R3blzVX72Fl0ov4XNFdH98LaV2HonRDfsF5D9UrJhD6T4Yunz3YgMMbw5MKtJMdGcuuILq6EaRMWwg8m9mXDvqN8nH3QlX2q5kWTg/qP4BC44idQuMnpOgm8+sVuSk6X8yMfmypquv9rvQgJkpZ97eHzvwICo//b3f1e+VNnFPWXfwNg4ZZCNuaV8OD43oSHBLsW5vohyaR2iOR/l2zXs4dWSJODOl//G6FjL/j0CU6fqeDlL3ZzZd94+ifHuBomrm04tw7vwsy1+9l/9LSr+w4IJfth3Wsw5BuuTnIIQOJASJsGK57DlB7jmaU76NKhDdOHJLsaJjQ4iO+O7cWGvBI+3Vbk6r5V4NPkoM4XHAKj/h8UbmbZgrc5fLKM717ZuDENDfXtMT0AeH7ZTiv7b1Jf/h2qKp330obLH4IzJexb/Czr9x3l22N6EBLs/r/zDUNTSIqJ4B+ftOAzPFUrTQ7K24CbMG0TiFn/HJld23NJtw5WwiTHRnL9kGTeXLWXo6da0HoTpcecaw0Z10H7bnZipAyDrqNou+45EtoEcdMwl89OPMJCgrh7VHdW7j7MprwSKzFUYNLkoLyFhLO9662MrFrH/xtUYTXU3aO6U1pexdur91mN41fr/wVlx52RzRblZ3ybDpVF/KbXNiJC3bvWUNPNl6QSFRbMy8t3WYuhAo8mB1WrPxZfRilhXHroXatx0hLbMaJ7B179cg+VVS3gomdVJax4BlKGO9/uLXq+oCe7TGfGnZhlNU67iFBuykzlo435HDzWCsamKECTg6rF1gPHWby7gl2JUwja9K7TM8aib13ejbwjp/k4u9BqHL/IXQxHdsPI+6yGOXmmgnfX5rM+4QZC81fXObLdLXdd1o2KKqNzLrUimhyUl9e+2k14SBDJ477rrEi28W2r8cZ7VpB77asWsOrfmn86S32mXWM1zIfr8zl+poLu478NIZGw6kWr8brFRTGqVxxvr97XMs7wVL00OajznC6r5MN1+Vw9IJF2PYdD4iBY/XKjFwRqjJDgIG6+JJXPcw8178neSvY7y7EO+cZFra7XGK9/tYe0xHYM6t0NBtzoLNxk+QzvtuFdKCgp5dNtOiiuNdDkoM4zd1MBx89UcMvwLs6U3pl3w8Es2LfSatybMp2FAt9pzhem170OpgqG3mE1zOb9JWwpOMZtw1OdKbmHfcuZomPzTKtxx6cnENc2nH+taMafkWowTQ7qPG+t2kePuCgu6eaZ1bP/DRDaBta/YTVucmwkV/SJ5+3Vec2z2aKqyhn01mOsMwWJRe+s3kdYSBDTBnkGvSUPhfg0659RaHAQN2WmsCSnkAMlemG6pdPkoM7aUXSClbsPc/Mlqf9ZJCY8GtKvc76Vltlt8rnlklQOHCtlWXMcjbv7MyjZ5yzmY1FpeSUfrM9nUkZnYtp4mq5EYMg3IW8VHLQ7zfbNmalUGWcKd9WyaXJQZ81cm0eQwPShNaZhGHyb02/fM9+SLV/rl0D7NqHMXNcMDzwb/g3h7aDf1VbDLNpSSMnpcm72NMOdNfDrEBTirAluUfe4KIZ2ieW9NXk631IL59Ya0pNEZKuI5IrIw7Vsv0tEikRkvefnXjfiKvdUVRk+WJfP6N7xdIqusZhP18shtqvTpm5RWEgQUwcmsTDrAMdKy63GctWZE7BlljMiOjTSaqiZa/NIiongsp4dz9/QNt5ZM2LjO85YC4tuGJbC9oMn2Lz/mNU4qmn5nBxEJBj4OzAZSAduFZH0Woq+ZYwZ7Plp2Erpym9W7j7M/qOnvc8awFlHYNCtsGuZs5yoRdOHJnOmoor5mw5YjeOq7FlOl99Bt1kNU3ziDMu2H2La4OTa19UY9HU4ccD5nCyaOiCJsJAg3lubZzWOalpunDkMB3KNMTuNMWXAm8C1LuxX+dH7a/cTFRbMxPTOtRcYcBNgYPN7VusxODWW7nFRzFzXjA48G9925lDqMtJqmNkbC6isMlxf1+yrva+C8Bjr41Ji2oQyPq0TszbkU1FZZTWWajpuJIdk4Ny+bXme52q6QUQ2isi7IpJay3YARGSGiKwWkdVFRc3wwmQzVFpeydzNBVzVvzORYXXM0RPXC5KGOP3pLRIRrh+SzFc7D5PfHKbyPnEQdn3qTHUuvq+SdyHvr9tPWmI7+naOrr1AaASkT4Psj6x3Hpg2KJnDJ8tYvqPYahzVdNxIDrX9R9S8UvUR0M0YMxBYDLxS186MMc8ZYzKNMZnx8fEuVE/V59NtRRwvreDawfWsBzDgJmeahqJtVutzzaAkAOZsLLAaxxVZ7ztjGwbcaDXM7kMnWb/vKNcPSbpwwYE3O50Hts2zWp+xfeOJjgjhQ+211GK5kRzygHPPBFKA8xqmjTHFxpgznofPA3ZnJFON8tGGfDpEhXF5zYucNfW/ARDYZLfZontcFAOSY/hoo93rG67Y9C50yoBOaVbDzPa8F1MH1pMcuo6C6ETrA+IiQoOZlNGZhVmFlJbbvQCumoYbyWEV0FtEuotIGHALcN40kSKSeM7DaUC2C3GVC06VVfBx9kEm9+9c/2Ix0Z2h2yjPt2W73RinDUpiY14Juw+dtBrHJ0d2Q95K62cN4FxvyOzanqTYenpDBQU541K2L3LWlbBo2uAkTpyp4JMcnU6jJfI5ORhjKoD7gQU4B/23jTFZIvKoiEzzFHtARLJEZAPwAHCXr3GVOxZnH+R0eeXZppx69Z8OxblQuNlqva4e6Hyf+GhDAJ89ZH3g3PafbjVM7sHj5Bw4ztSBifUXrq5P5RnYardp6dIeHYlrG8bs5tD8pxrNlXEOxpi5xpg+xpiexpjHPM/90hgzy3P/EWNMhjFmkDHmSmOM3WGcqsFmb8inU3R4w1d7S5sGEvSfA6MlSbGRZHZtz5xNAXzg2fKBc5He1mpvHh9tKEAEpgxoYHJIzoR2KZBlt2kpJDiIqzI6syTnIKfLtGmppdER0q3YiTMVLN1WxJQBiQTX1m++NlFx0H2MX5qWpgxIJOfAcXYWnbAa56Ic2Q3565wmHMvmbCpgRPcOdGoXUX9hcJqWMq6D3I/h9BGrdbt6QCKnyyv5ZKs2LbU0mhxasU9yDlJWUdXwb6TVMq6HwzvgwEY7FfOYPMAZczFvcwAOiNvyoXObYTc5bC88Tu7BE1zd6M9oOlSVw9b5dirmMbx7BzpGhQX2GZ66KJocWrF5mwuIjw5nWNf2jXthv2tAgp0pIyxKjIlkaJdY5gbigSfLP01K8zYfQASuyqhjcGJdkoc6TUvZdj+jkOAgrurfmSXZ2rTU0mhyaKVOlVWwJOcgkzI6N7xJqVpUR+h2ufUDDzhNS1n5x9hTHEC9lo7ug/y1kG5/IoC5m5xeSg1uUqom4qxGl/sxnDlup3Ie1U1LughQy6LJoZVaurWI0vKqs003jZY2DQ5tsz5F9KT+Tv3mBtJcS9Wz06ZNu3A5H+06dJKcA8eZ1L+RTUrV0q91ei1tW+BuxWoY0b0DsW1CWZDVAtYAV2dpcmil5m8+QIeoMIY3tJdSTf2mOreWzx5S2rdhQHIMC7ICKDlsmeUMfOvY02qYeZud5rTqBNloqSOgbcJ/ro9YEhIcxIS0BBZnF1JWoXMttRSaHFqhsooqPsk5yPi0TvUPfKtLu0Tn4GP5ugM4B8f1+44GxupjJw7C3i+dJhvLFmw+wKCUGJLrG/hWl6AgJ4nnLoZyu/NUTerfmeOlFXyx45DVOMp/NDm0Ql/sOMTxMxUX/420Wto1ULgJDu9yp2J1qL4Yu3BLAJw95MwBjDPBnUUFJafZkFfCxMZeiK4pbaqzvvSOJe5UrA6X94qjbXhIYJ3hKZ9ocmiFFmQdICosmMt6xvm2o+qmpZw5vlfqAnp1akuvTm2ZHwhdWrM/gvbdoVNtS5a4Z6Gn/b7RvZRq6jYaImIg2+4qfhGhwVzZrxMLswqb5xrgyosmh1amssqwaEshV/brRERoHdNzN1SH7pDQ33pyAJiU0ZkVuw5z5GSZ9Vh1Ki1xFtJJm2p9eu4FWQfoGR9Fr05tfdtRcCj0meTM0lpZ4U7l6nBVRgLFJ8tYu9fuwDvlH5ocWpm1e49w6ESZ799Iq/W72mmDP2F37Y2JGQlUVhk+bspJ3rYvcgaW9bN7veHIyTJW7Drs4mc01RkpvfcLd/ZXhyv6xBMWHMRCbVpqETQ5tDKLthQSGiyM7evSWhn9pgLG+voBA5Jj6NwugkVNed0hZzZEdYKUTKthluQcpLLKuJcceo2DkEjrTUvREaFc1qsjC7cUYixPraLs0+TQihhjWJB1gMt6xhEdEerOTjsPgJgu1g88IsLEjAQ+3VbUNCNxK844Zw59J0OQj81x9Vi0pZDO7SIYmBLjzg7DoqDnlbB1rvX5sCamd2ZP8Sm2FtodeKfs0+TQimw/eII9xaeYkJ7g3k5FnKalnUvhjN0J8iamd6a0vIrPc5ugu+SuZVB2wvldLSotr+TTbUWMT++EuHldo+8UKNlnfT4sp97/uaCumi9NDq1IdVuwq8kBoN8UZySu5e6SI3p0IDqiibpL5syGsLbQ/QqrYZbnHuJ0eSUT011qUqrWd7Iz1XrOXHf3W0On6AgGp8ayaIsmh+ZOk0MrsnBLIYNSY0lo7Dw99elyGUTEWu+1FBocxNf6dTrbJu83VVXOwjm9xkGoy+9dDQuzCokOD2Fkj3qWbG2sqDhn0KIfepZNSE9g0/4S8o/aHXin7NLk0EocKCllY14JE90+awAIDnG6S25fYL275IT0BA6fLGPNHj92l8xfCycKoa/dJiWnN1YhV/SNJyzEwr9m3ynOoMUje9zf9zmqz3oWZ+vZQ3OmyaGVWOT5R7WSHMBpWjp9xOnWatEVfeIJDRb/9lrKmeNMUd57gtUw6zzdjH0eFV2X6uslW+02LfXq1JYecVHatNTMuZIcRGSSiGwVkVwRebiW7eEi8pZn+woR6eZGXNVwi7YU0q1jG98HVdWl5zgIDrd+4ImOCOXSnnEs8md3ya1zoetl0OYiJylsINe7GdfUsSfE9bX+GYFzhvfVzmKOlZZbj6Xs8Dk5iEgw8HdgMpAO3CoiNecWuAc4YozpBTwFPOFrXNVwx0vL+XLHISakJ7jbA+Zc4W2hx1jnW7blg/aE9AR2F58i96Aflg8t3gFFOdZ7KYGTHEb26Eg7t7oZ16bfFNi93PryoRPSEyivNCzdandwpLLHjTOH4UCuMWanMaYMeBOouQrKtcArnvvvAuPE2lFK1fTptiLKKw0T3O4BU1PfyXB0DxzcYjXMhDSnaWyhP5otqr9l951iNcyOohPsPHTS/Z5kNfW9GkylM2bDoiFd2tMxKkyblpoxN5JDMrDvnMd5nudqLWOMqQBKgFq7Y4jIDBFZLSKri4r0W4cbjp2uoGd8VOOXA22svpOdW8vdJTvHOAPE/HLgyZnrzB/VvqvVMNW/y/g0y8kheZizxoPlXkvBQcK4tE4s9axTruGlnVkAABqDSURBVJofN5JDbWcANdsVGlLGedKY54wxmcaYzPh4S22vrcxtI7qw+L+vaPxyoI0V3RmSM2GrH7pLpiWwft9RDh6zuMbDyWLY95X1swZwkkNGUjuSLnbthoYKCnJ6luUudkZ9WzQhvTPHz1SwYlex1TjKDjeSQx6Qes7jFCC/rjIiEgLEAIddiK0ayG+teP2mQP46OFbzT8BdEzKcb9iLsy1OxLdtPpgq53eyqOj4GdbuPeL+wLe69LvaGe296zOrYUb1iiMiNEiblpopN5LDKqC3iHQXkTDgFqDm8mCzgDs9928Elhidmatl6uuf7pJ9E6JJ7RBpt0vr1rkQnQSJg+3FAD7OLsQYCyPX69L9CgiNsn6GFxkWzOje8SzWifiaJZ+Tg+cawv3AAiAbeNsYkyUij4pI9XJZLwIdRSQX+G/Aq7uraiHi+0KHntavO4gIE9I6s3xHMSfPWBh4V37amQ6k3xTrazcs2lJIcmwkaYnRVuOcFRoBvb7mjPqusns9YEJ6AvklpWTlH7MaR7nPlXEOxpi5xpg+xpiexpjHPM/90hgzy3O/1BhzkzGmlzFmuDFmpxtxVQAScQ6ou5Y5i+NYNCE9gbKKKpZts9BxYedSZ3lNy11YT5VV8Hmu5W7Gtek3FY4XOE2AFo3r14kg8VPPMuUqHSGt3Nf3amdRnNzFVsNc0q09sW1C7bRp58yG8HbQdZT7+z7Hsm2HOFNRZW/kel16T3RGfVtuWurYNpzMrh10AaBmSJODcl/qcGgTZ727ZIhnIr6Pcw5SXuli80hVJWyd70yXERLm3n5rsXDLAWIiQ7mku93R117adHBGfVtu/gPnDC/nwHH2HT5lPZZyjyYH5b6gYOg7yRloVWF3zeeJ6Z0pOV3Oqt0udn7btxJOHbLepFRRWcWSnIOM69eJ0OAm+FfsNxWKsp1R4BZVX2jXpqXmRZODsqPfVDhzDHbb7S45pk8c4SFB7i4ukzMbgkKh13j39lmLVbuPcPRUORMz/NykVK26i67lM7xucVH0TYjWpqVmRpODsqPHWKe7ZI7d5UPbhIUwureLE/EZ49S5xxUQ4dIynXVYuOUA4SFBjOnTRIM9Y7tA54F+WeNhYkYCq3YfpviE3YF3yj2aHJQdoZHO4jg5c613l5yY0Zn9R0+7012yMAuO7HbOfCwyxrAwq5DRveNoExZiNdYF9ZsK+1bACYuDCYGrMjpTZeDjHLtxlHs0OSh70q6BEwecxXIsGp+WQJDA/M0uNFvkzAHE+pQZWfnH2H/0tP9GRdel39WAsX72kJHUjuTYSG1aakY0OSh7ek+AoBDIrjlg3l0dosIY3r2DO2tL53zk9LaKtnsdYEHWAYKDhPH+7sJaU0IGtO9uvflPRJiQnsCy7YfsDFpUrtPkoOyJbA/dx0D2R9bXeJiU0ZntB0+wo8iHNR6O7IYDm6w3KQHM23yAEd070CHKblfZeok4Z3g7P4XTR62GuiqjM2UVVXxqY9Cicp0mB2VX2jQ4vNP6Gg/VS2v6dPaQ/ZFzm3aNCzWqW+7B4+QePMFVtpYDbay0ac6gxW0LrIa5pFt7OkSFMc+N5j9lnSYHZVe/qwGBLXablpJiIxmUGuvbdYcts5zeOx26u1exWizwdLttsi6sNSUPg+hE681/IcFBTExPYEl2IaXllVZjKd9pclB2te3kjMSt/lZu0ZT+ndmYV3JxI3GP5UPeSkifVn9ZH83bXMDg1FgSYyyv3dBQQUFOU1rux1B20mqoyQMSOVlWyefbD1mNo3ynyUHZlzYNDmbBoVyrYSb3TwQustdStueCbFrNFW7dtbf4FJv3H+PqAYlW4zRa+rVQcRq2L7Qa5tIeHWkXEaJNS82AJgdlX3Ub/pb3rYbp0rENGUntmLe5oPEvzp4F8f0gvo/7FTvHXE/dJvUPkOsN1bpeBlGdIOsDq2HCQoIYn57Aoi0HdPnQAKfJQdkXkwypIyDrQ+uhpgxIZO3eoxSUnG74i44Xwu7PIf06exXzmLupgEEpMaR2aGM9VqMEBTtJfPtCKLM7Qd7VAxI5VlrB8h3atBTINDko/8i4Hgo3WW9amuJprpmzsRFnD1s+BIxTR4v2HT7FxrwSJgdak1K19GudNSwsNy2N6h1HdEQIszdcxBme8htNDso/0jwXei03LXWPiyIjqR2zG5Mcst6HTunQqZ+9iuGcNQBM6R+gyaHr5c5U61l2P6PwkGCuyujMwi0HOFOhvZYClSYH5R8xyZA6EjbbPfAAXDMoifX7jjas19KxfNj7JWRMt16vWRvyGZQaS5eOAdakVC04xDl72LYAzvgwmLABrh6YyPHSCj7bpk1Lgcqn5CAiHURkkYhs99y2r6NcpYis9/zY7UytAlf/G5xeS4V2B8RV9wRq0NnD2SYlu9cbdhSdICv/GNMGJVmN47MBNzq9lrbaXQRoVK84YtuEMntjvtU46uL5eubwMPCxMaY38LHncW1OG2MGe37sdyRXgSnjOmdpys3vWg2T2qENg1Nj+WhDAw48G9+GxEEQ19tqnWatz0cEpg4M0CalaqkjoV0ybLL7GYUGBzG5f2cWbinkVJnOtRSIfE0O1wKveO6/Atjv7qGar7adnHUeNr1rfa6lawcnsaXgGNsLj9ddqHiHM2PsgJut1sUYw0cb8xnRvQMJ7SKsxvJZUBD0nw47PoZTLq6uV4trBydzqqzSzhrgyme+JocEY0wBgOe2Ux3lIkRktYh8JSIXTCAiMsNTdnVRkU7Q1eIMuBGO7oG81VbDTB2YRHCQ8P66/XUX2vg2IE5zl0Wb9x9jZ9FJpg1KthrHNf1vhKoK2GJ3zMPwbh1Iiongw/XatBSI6k0OIrJYRDbX8tOYoaRdjDGZwG3AX0WkZ10FjTHPGWMyjTGZ8fFNtEKWsqffVAiJgI1vWg0THx3O6N5xfLg+n6qqWs5SjIFNb0P30dDOblPPe2vzCAsJCrxR0XVJHOQMCNxg9zMKChKuGZzEsm1FHD5pd61x1Xj1JgdjzHhjTP9afj4ECkUkEcBzW+syT8aYfM/tTmApMMS130A1LxHtnMn4Nr0LFXaXjLx+SDL7j55m5e5amkfyVjuzxVpuUiqvrGLWhnwmpCUQ0ybUaizXiMCgW5wV4op3WA113eBkKqoMc/TCdMDxtVlpFnCn5/6dgNcQWBFpLyLhnvtxwOWA3e4qKrANvg1Kj8K2+VbDTEhPICosmJlr87w3rn8dQttY76X06VbnW/H0oc2kSanagJsBgY1vWQ2TltiOX0xNZ3RvbSUINL4mh8eBCSKyHZjgeYyIZIrIC54yacBqEdkAfAI8bozR5NCa9bjSmSJ6/b+shmkTFsLVAxOZvbHg/NXHyk7B5plOn/7waKt1mLkuj45RYYzp08wOfjHJTueBDf+2vgb4PaO60y0uymoM1Xg+JQdjTLExZpwxprfn9rDn+dXGmHs9978wxgwwxgzy3L7oRsVVMxYU7DRbbF/kzGtk0c2ZqZwqqzx/Oo2c2XDmGAz+htXYxSfOsGhLIdcOTiY0uBmONx18GxzdC7s/a+qaqCbQDP9iVYsw+BtgKmH9G1bDDOvanh7xUby1et9/nlz3OsR2daaLsOi9tXmUVxpuHZ5qNY41addARCys+WdT10Q1AU0OqmnE9YZuo50Dj8VmCxHh65mprNlzhNyDx50LrLs+hSG3O336LTHG8OaqfQzr2p7eCXabrqwJjXTO8HJmw0md5qK10eSgms6wu5wxDzuXWA0zfWgKocHC61/thdUvQVAIDL3DasyVuw6zs+gktw7vYjWOdUPvhMoy59qDalU0Oaimk3YNtOkIq1+2GiY+OpwpAxKZvWYnZt3rzliLaLvrN7/61R6iI0Kaz9iGuiSkO2txrH7J+oVpFVg0OaimExLuNO9snetc+LTojku7cUX550jpUbjkHqux8o+eZv7mA9xySSqRYcFWY/nF8BnOmJDcRU1dE+VHmhxU0xr+bUBgxf9ZDTM0NYbvRS5gd1Aqpusoq7Fe/2oPxhjuuLSb1Th+k34tRCfBV880dU2UH2lyUE0rJsVZgW3tq3DmApPk+Uh2fUqPyl38/cxklm63d3G1tLySf6/cy4T0hMBbCvRiBYc6Z1s7P4GD2U1dG+UnmhxU07v0u864g7Wv2Yvxxf9i2iawImoczy61NyXEW6v2ceRUOfeM6mEtRpMY9i0IiYQv/repa6L8RJODanrJw6DrKPjiaSgvdX//BRthx8fIiP/ijtF9WLHrMOv2HnE9TFlFFc9+uoNLurVnePcOru+/SUV1hMxvOZPxHdnd1LVRfqDJQQWGK34Mxwuc5iW3Lf0DhMdA5t3cMrwLMZGh/G1JruthZq7No6CklPu/ZnfhoCZz2fed0e2f/7Wpa6L8QJODCgzdx0CXS+Hzp9w9e9i/1ukNddn9ENmetuEhzBjTg49zDrJmj3uL2ZypqOTvS3MZmBLDmN5xru03oLRLgiHfdEaYW+5dppqeJgcVGERg7MNwPB9WPufefj95DCLbw4jvnH3qW5d3I65tOE/M34pxaUW6177cw77Dp/nhxL6IiCv7DEijf+CcPXz8W/f2eXiXe/tSrtHkoAJHj7HQawIs+xOccGEVwG0LIXcxjPpvZx0JjzZhITw4rhcrdx1mSU6tS5A0Ssmpcv53SS6je8c1v9lXGysmBS79nrNQ0v61vu/vyG74x0hY/rTv+1Ku0uSgAstVv4fyU/DJ73zbT3kpzPsxdOx93llDta9f0oVendryq1lZnC6r9CnUU4u3cay0nJ9OSfNpP83G5Q9BmziY/4hvo6aNgbk/cqYzsbxUq2o8TQ4qsMT3cUbkrnkFdi+/+P0s/x84sgsmPwEhYV6bw0KCeOy6/uQdOc3TS7ZfdJjVuw/zype7uWNkV9IS29VbvkWIaAcTHoV9X8GqF+ovX5fsj2D7Qrjyp876ESqgaHJQgefKn0H7rvDBdy5uYFzeavj0CefbaK9xdRYb0aMjNw5L4fllO1mzp/FdW0vLK/nxextJionkx5P6Nb6ezdng26DnOFj864u7ZnC8EOb8ABIGwPD/cr16yneaHFTgCW8L1/8flOQ5B5DGXDQuPQbv3ev0rLn6L/UW/8XUdBJjI7j/X2sbtci9MYZHZm5iZ9FJHr9hAFHhIQ2vY0sgAtOedi5Ov3Ons7peQ1VVwsx7ncQ//TkIbmXvXTOhyUEFpi4jYexPnTWMl/2pYa8pL4W3vuF0s5z+PETG1vuSmMhQ/n7bUIpPlPG9N9ZSWt6w6w8vfLaL99ft5wcT+rTe9Y9jUpz3uWAjfPi9hiVxY2DBT2HXMrj6SWfWVxWQfEoOInKTiGSJSJWIZF6g3CQR2SoiuSLysC8xVSsy5ocw6DanO+oX/3vhg0/5aXjvHuegc90/oOulDQ4zMCWWx28YwFe7ivmv19bUmyBe+Gwnj83NZsqAztz/tV4NjtMi9Z0E438FWTPhg+9CZXndZY2Bxb+CFc/CyO9aX6ZV+cbX87nNwHSgzik1RSQY+DswAcgDVonILGPMFh9jq5ZOBK75Hyg/CQt/DkU5MOG30KbG1BSHtsO734IDm2DSE87qZY00fWgK5ZVV/OS9TVz/jy/469cH07fz+Su4lZwq5/H5Ofx75V6mDOjMU18f3LLHNDTU5Q85SeGTx5xxKlP/Ch26n1/mRBHMuh+2zYfMe5xeafreBTRxYxCQiCwFfmiMWV3LtkuBXxtjrvI8fgTAGPOH+vabmZlpVq/22qVqbaqqnAPPZ3+G8HYw6OvQeSCUnYS9Xzi9XsKjYfoL0GeiT6GW5BTy43c3cuRUOVf27cSYPnFEhAazKa+EOZsKOHqqjHtH9+Ank/oRHKQHt/OsfRXmPQxVFc40310vA4wzHmLzTKgqd3o5jfiOJgaLRGSNMabOlpwG78cPyeFGYJIx5l7P49uBEcaY++vY1wxgBkCXLl2G7dmzx+f6qRaicAss/T3kfuyMhQCIiHWWGx15H0R3diXMoRNneP6zncxcu5+i42cAaBMWzOW94nhofG8ykmJcidMiHStwPqOcuXDKMzV6aBT0n+4MnuvUSsaCNCG/JQcRWQzU9l/3M2PMh54yS6k7OdwEXFUjOQw3xny/vsrpmYOqVVWlM7I2vB1ExVn7FlpZZSg+eYZTZypJjI0gPKQFrOrmL1VVzvrgIeHOgLlaxpooO9xKDvVeczDGjPcxRh6Qes7jFCDfx32q1iwoGDr2tB4mOEjoFB0B0fWXVTUEBXlfd1DNij+6sq4CeotIdxEJA24BZvkhrlJKqYvka1fW60UkD7gUmCMiCzzPJ4nIXABjTAVwP7AAyAbeNsZk+VZtpZRSNvnUldUY8z7wfi3P5wNTznk8F5jrSyyllFL+oyOklVJKedHkoJRSyosmB6WUUl40OSillPKiyUEppZQXTQ5KKaW8aHJQSinlRZODUkopL5oclFJKedHkoJRSyosmB6WUUl40OSillPKiyUEppZQXTQ5KKaW8aHJQSinlRZODUkopL5oclFJKedHkoJRSyouva0jfJCJZIlIlIpkXKLdbRDaJyHoRWe1LTKWUUvb5tIY0sBmYDvxfA8peaYw55GM8pZRSfuBTcjDGZAOIiDu1UUopFRD8dc3BAAtFZI2IzPBTTKWUUhep3jMHEVkMdK5l08+MMR82MM7lxph8EekELBKRHGPMsjrizQBmAHTp0qWBu1dKKeWmepODMWa8r0GMMfme24Mi8j4wHKg1ORhjngOeA8jMzDS+xlZKKdV41puVRCRKRKKr7wMTcS5kK6WUClC+dmW9XkTygEuBOSKywPN8kojM9RRLAD4XkQ3ASmCOMWa+L3GVUkrZ5WtvpfeB92t5Ph+Y4rm/ExjkSxyllFL+pSOklVJKedHkoJRSyosmB6WUUl40OSillPKiyUEppZQXTQ5KKaW8aHJQSinlRZODUkopL5oclFJKedHkoJRSyosmB6WUUl40OSillPKiyUEppZQXTQ5KKaW8aHJQSinlRZODUkopL5oclFJKedHkoJRSyosmB6WUUl58Sg4i8icRyRGRjSLyvojE1lFukohsFZFcEXnYl5hKKaXs8/XMYRHQ3xgzENgGPFKzgIgEA38HJgPpwK0iku5jXKWUUhb5lByMMQuNMRWeh18BKbUUGw7kGmN2GmPKgDeBa32Jq5RSyq4QF/d1N/BWLc8nA/vOeZwHjKhrJyIyA5jheXhGRDa7VkM74oBDTV2JBtB6ukvr6S6tp3v6urGTepODiCwGOtey6WfGmA89ZX4GVABv1LaLWp4zdcUzxjwHPOfZ72pjTGZ9dWxKzaGOoPV0m9bTXVpP94jIajf2U29yMMaMr6cidwJTgXHGmNoO+nlA6jmPU4D8xlRSKaWUf/naW2kS8BNgmjHmVB3FVgG9RaS7iIQBtwCzfImrlFLKLl97K/0NiAYWich6EXkWQESSRGQugOeC9f3AAiAbeNsYk9XA/T/nY/38oTnUEbSebtN6ukvr6R5X6ii1twQppZRqzXSEtFJKKS+aHJRSSnkJqOQgIr8Wkf2e6xfrRWRKHeWabDqORkwZsltENnl+D1e6ljWwfhd8b0QkXETe8mxfISLd/FW3c+qQKiKfiEi2iGSJyIO1lBkrIiXn/C380t/19NTjgp+jOJ72vJ8bRWRoE9Sx7znv03oROSYiD9Uo0yTvp4i8JCIHzx2vJCIdRGSRiGz33Lav47V3esps9/SK9GcdA+7/vI562jtmGmMC5gf4NfDDesoEAzuAHkAYsAFI92MdJwIhnvtPAE/UUW43EOfn96/e9wb4LvCs5/4twFtN8DknAkM996Nxpl6pWc+xwGx/162xnyMwBZiHM55nJLCiiesbDBwAugbC+wmMAYYCm8957o/Aw577D9f2PwR0AHZ6btt77rf3Yx0D7v+8jnpaO2YG1JlDAzXpdBymYVOGNJWGvDfXAq947r8LjBOR2gYqWmOMKTDGrPXcP47Tiy3Zn3Vw0bXAq8bxFRArIolNWJ9xwA5jzJ4mrMNZxphlwOEaT5/7N/gKcF0tL70KWGSMOWyMOYIzj9skf9UxEP/P63gvG+KijpmBmBzu95zKvVTH6WZt03E01YHlbpxvjbUxwEIRWeOZEsQfGvLenC3j+eMvATr6pXa18DRrDQFW1LL5UhHZICLzRCTDrxX7j/o+x0D6ewTnbPDfdWwLhPcTIMEYUwDOFwWgUy1lAul9DbT/85qsHDPdnFupQeQC03EAzwC/xXnDfwv8GeeDOW8XtbzW1f64F6qjadiUIQCXG2PyRaQTzjiQHE/mt6kh743196+hRKQt8B7wkDHmWI3Na3GaRk542lE/AHr7u47U/zkG0vsZBkyjltmRCZz3s6EC4n0N0P/zc1k7Zvo9OZh6puOoJiLPA7Nr2WR9Oo766ij1TxmCMSbfc3tQRN7HObWz/UfTkPemukyeiIQAMVzcqapPRCQUJzG8YYyZWXP7ucnCGDNXRP4hInHGGL9OetaAzzGQpoeZDKw1xhTW3BAo76dHoYgkGmMKPE1wB2spk4dznaRaCrDUD3U7K4D/z8+Nf/azdvuYGVDNSjXaaq8HapuRtUmn45AGTBkiIlEiEl19H+filj9ml23IezMLqO75cSOwpK4/fFs81zheBLKNMX+po0zn6mshIjIc52+12H+1bPDnOAu4w9NraSRQUt1k0gRupY4mpUB4P89x7t/gncCHtZRZAEwUkfaeppKJnuf8IsD/z8+tg71jpj+usjfiavxrwCZgo6fyiZ7nk4C555SbgtPDZQdOU48/65iL03633vPzbM064vQK2OD5yfJnHWt7b4BHcf7IASKAdzy/x0qgRxN8zqNwTms3nvM+TgG+A3zHU+Z+z3u3AeeC4GVNUM9aP8ca9RScxax2eP52M/1dT0892uAc7GPOea7J30+cZFUAlON8g70H5xrXx8B2z20HT9lM4IVzXnu35+80F/iWn+sYcP/nddTT2jFTp89QSinlJaCalZRSSgUGTQ5KKaW8aHJQSinlRZODUkopL5oclFJKedHkoJRSyosmB6WUUl7+P5VsCElkiHo0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#两条曲线整合在一起\n",
    "plt.plot(x,y,label = 'sin(x)')\n",
    "plt.plot(x,y2,label = 'cos(x)')\n",
    "plt.legend()  #在左下角有提示标签\n",
    "plt.axis([-5,15,-2,2]) #x轴现在是0到10,改到-5到15,y轴有-2到2,图像就会变小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#绘制柱状图和饼图\n",
    "x = ['Q1','Q2','Q3','Q4']\n",
    "y = [10,30,20,60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#柱状图\n",
    "plt.bar(x,y,color = 'g',width = 0.3)\n",
    "plt.grid(True) #加网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#柱状图\n",
    "plt.bar(x,y,color = 'g',width = 0.3)\n",
    "plt.grid(True) #加网格线\n",
    "plt.text(0.2,40,'test')#绘制文字\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#柱状图\n",
    "rect = plt.bar(x,y,color = 'g',width = 0.3)\n",
    "plt.grid(True) #加网格线\n",
    "\n",
    "#绘制标度值\n",
    "for index ,item in enumerate(rect):\n",
    "    #获取坐标\n",
    "    _x = item.get_x()+0.1 #获取x轴对象  +0.1 向右偏移一点\n",
    "    _y = item.get_height()  #获取各个x轴的高度\n",
    "    #获取坐标上的柱子的值\n",
    "    plt.text(_x,_y,y[index])\n",
    "plt.ylim(0,70) #y轴扩大点\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#饼图\n",
    "plt.pie(y,labels = x)  #默认是椭圆形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axes(aspect = 1)  #修改成圆形\n",
    "plt.pie(y,labels = x,autopct = '%2.f%%',explode = [0.2,0,0,0]) #小数点后两位,%%是转义,输出一个%  ,explode 偏移,像蛋糕拿出来\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "267"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##直方图\n",
    "np.random.seed(100)\n",
    "data = np.random.normal(9000,3000,size = 300) #nomal正态分布 平均值9000,标准差3000\n",
    "data = data[data>=5000]\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data,color= 'g',rwidth= 0.5,alpha = 0.6) #宽度rwidth 透明度 alpha\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设置柱子的个数\n",
    "plt.hist(data,color= 'g',rwidth= 0.5,alpha = 0.6,bins=20) #宽度rwidth 透明度 alpha bins 柱子的个数\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱线图    看异常值\n",
    "#显示的中线是中位数,上边界,下边界, 四分位数\n",
    "data = np.arange(1,10)\n",
    "plt.boxplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.74976547,  0.3426804 ,  1.1530358 , -0.25243604,  0.98132079,\n",
       "        0.51421884,  0.22117967, -1.07004333, -0.18949583,  0.25500144,\n",
       "       -0.45802699,  0.43516349, -0.58359505,  0.81684707,  0.67272081,\n",
       "       -0.10441114, -0.53128038,  1.02973269, -0.43813562, -1.11831825,\n",
       "        1.61898166,  1.54160517, -0.25187914, -0.84243574,  0.18451869,\n",
       "        0.9370822 ,  0.73100034,  1.36155613, -0.32623806,  0.05567601,\n",
       "        0.22239961, -1.443217  , -0.75635231,  0.81645401,  0.75044476,\n",
       "       -0.45594693,  1.18962227, -1.69061683, -1.35639905, -1.23243451,\n",
       "       -0.54443916, -0.66817174,  0.00731456, -0.61293874,  1.29974807,\n",
       "       -1.73309562, -0.9833101 ,  0.35750775, -1.6135785 ,  1.47071387,\n",
       "       -1.1880176 , -0.54974619, -0.94004616, -0.82793236,  0.10886347,\n",
       "        0.50780959, -0.86222735,  1.24946974, -0.07961125, -0.88973148,\n",
       "       -0.88179839,  0.01863895,  0.23784462,  0.01354855, -1.6355294 ,\n",
       "       -1.04420988,  0.61303888,  0.73620521,  1.02692144, -1.43219061,\n",
       "       -1.8411883 ,  0.36609323, -0.33177714, -0.68921798,  2.03460756,\n",
       "       -0.55071441,  0.75045333, -1.30699234,  0.58057334, -1.10452309,\n",
       "        0.69012147,  0.68689007, -1.56668753,  0.90497412,  0.7788224 ,\n",
       "        0.42823287,  0.10887199,  0.02828363, -0.57882582, -1.1994512 ,\n",
       "       -1.70595201,  0.36916396,  1.87657343, -0.37690335,  1.83193608,\n",
       "        0.00301743, -0.07602347,  0.00395759, -0.18501411, -2.48715154])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "data = np.random.normal(size=100) #符合正态分布,以0为中心,误差在2之内\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)  #直方图也是符合正太分布\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#现在加入不合理的值,再用箱线图显示\n",
    "data = np.concatenate([data,[4,7,8,9,-4]])  #添加噪点\n",
    "plt.boxplot(data)\n",
    "plt.show()  #可以看到大多数在-2到2之间,异常值是在边界之外"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# whis:指定上下边界 四分位距离,默认1.5倍四分位差\n",
    "plt.boxplot(data,whis = 3)  #可以看到4在上边界里\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data,showmeans = True,meanline = True)  #显示平均值先\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2Jo/hLeAc8HPgiXrPdSM/TFvJwrk2zuJcnWu1ffnaPmZmGdSQH8gwM7Pt5eJvZpZBLv5mZhnk4m9mlkEu/mZmGeTib2aWQS7+ZmYZ5OJvZpZB/w/PwXcPEXaTbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#子图\n",
    "# fig,ax1 = plt.subplot(2,3,1)   plt.subplot是返回2个值\n",
    "# (2,3,1)表示子图是2行3列组成，1表示子图的位置\n",
    "plt.subplot(2,3,1)  #第一张小图\n",
    "plt.subplot(2,3,2)  #第二张小图\n",
    "plt.subplot(2,3,3)  #第二张小图\n",
    "# plt.subplot(2,3,4)  #第二张小图\n",
    "# plt.subplot(2,3,5)  #第二张小图\n",
    "# plt.subplot(2,3,6)  #第二张小图\n",
    "\n",
    "# plt.subplot(2,2,2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>城市</th>\n",
       "      <th>月均工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>北京</td>\n",
       "      <td>9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>上海</td>\n",
       "      <td>8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>深圳</td>\n",
       "      <td>8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>7409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>杭州</td>\n",
       "      <td>7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>宁波</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>佛山</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>东莞</td>\n",
       "      <td>6809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名  城市  月均工资\n",
       "0   1  北京  9240\n",
       "1   2  上海  8962\n",
       "2   3  深圳  8315\n",
       "3   4  广州  7409\n",
       "4   5  杭州  7330\n",
       "5   6  宁波  7000\n",
       "6   7  佛山  6889\n",
       "7   8  东莞  6809"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_table('./pysourse/salay.txt',sep = '\\t')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#显示中文可能乱码,把字体换成微软，关键字参数\n",
    "plt.rc('font',**{'family':'Microsoft YaHei,SimHei'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x485bfb0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x5983cd0>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.plot(data['城市'],data['月均工资']) #子图里绘制图，需要这2行一起执行\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>城市</th>\n",
       "      <th>月均工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>北京</td>\n",
       "      <td>9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>上海</td>\n",
       "      <td>8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>深圳</td>\n",
       "      <td>8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>7409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>杭州</td>\n",
       "      <td>7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>宁波</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>佛山</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>东莞</td>\n",
       "      <td>6809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名  城市  月均工资\n",
       "0   1  北京  9240\n",
       "1   2  上海  8962\n",
       "2   3  深圳  8315\n",
       "3   4  广州  7409\n",
       "4   5  杭州  7330\n",
       "5   6  宁波  7000\n",
       "6   7  佛山  6889\n",
       "7   8  东莞  6809"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>城市</th>\n",
       "      <th>月均工资</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>1</td>\n",
       "      <td>北京</td>\n",
       "      <td>9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>2</td>\n",
       "      <td>上海</td>\n",
       "      <td>8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>3</td>\n",
       "      <td>深圳</td>\n",
       "      <td>8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>7409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>5</td>\n",
       "      <td>杭州</td>\n",
       "      <td>7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁波</th>\n",
       "      <td>6</td>\n",
       "      <td>宁波</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>佛山</th>\n",
       "      <td>7</td>\n",
       "      <td>佛山</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东莞</th>\n",
       "      <td>8</td>\n",
       "      <td>东莞</td>\n",
       "      <td>6809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    排名  城市  月均工资\n",
       "城市              \n",
       "北京   1  北京  9240\n",
       "上海   2  上海  8962\n",
       "深圳   3  深圳  8315\n",
       "广州   4  广州  7409\n",
       "杭州   5  杭州  7330\n",
       "宁波   6  宁波  7000\n",
       "佛山   7  佛山  6889\n",
       "东莞   8  东莞  6809"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#索引换成城市\n",
    "# 方法1\n",
    "# data.index = data['城市']   #缺点：为了好看还需要把原来图的城市列删除  df.drop('城市',axis=1)\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>月均工资</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>1</td>\n",
       "      <td>9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>2</td>\n",
       "      <td>8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>3</td>\n",
       "      <td>8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>4</td>\n",
       "      <td>7409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>5</td>\n",
       "      <td>7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁波</th>\n",
       "      <td>6</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>佛山</th>\n",
       "      <td>7</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东莞</th>\n",
       "      <td>8</td>\n",
       "      <td>6809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    排名  月均工资\n",
       "城市          \n",
       "北京   1  9240\n",
       "上海   2  8962\n",
       "深圳   3  8315\n",
       "广州   4  7409\n",
       "杭州   5  7330\n",
       "宁波   6  7000\n",
       "佛山   7  6889\n",
       "东莞   8  6809"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#方法2  把列变成索引，列中就没有了索引这一列\n",
    "data = data.set_index('城市')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#把列表画成柱状图，由于城市列不见了，要由索引代替\n",
    "plt.subplot(2,2,2)  #子图的第二个图\n",
    "plt.bar(data.index,data['月均工资'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#通过箱线图看有无异常值,一个列可以直接形成箱线图,需要包装成dataframe 加[]\n",
    "data[['月均工资']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x05744DF0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+u5rlbOAtmbmzf8OUJM0mMnPuGSL+KY2gfns1/d+AP8rMqWr6fuD2zHx8jnVsBDYCjI6Orpuamup6wLsPHu562U5GV8DTR9v3rR07q2/bnUs/623WrvZB1bzYZmZmGBkZGegYFutxbnbiMR/2fbuduZ7r/bSQ//Xk5OSuzBxv19fxiBz4SeBA0/RTwEubps8A/jQingW2ZOb21hVk5jZgG8D4+HhOTEzUHPoLbdi8o+tlO9m09nnu3N3+X7L/lom+bXcu/ay3WbvaB1XzYpuenmYh+2QvLNbj3OzEYz7s+3Y7cz3X+6lf/+s6lSwHjjdNH6dxigWAzLwaICIuBx6MiJ2Zubeno5QkzarOm52HgLGm6fOBJ1tnysw9wJeAy3ozNElSHXWC/AvANRHxkog4F3gdcP+JzohYU/2+AHg18HA/BipJaq/jqZXMfDoi3gd8uWraBLwpItZk5hbgExFxHnAU2JSZ+/s2WknSC9Q625+ZdwN3z9K3vofjkSTNk5/slKTCGeSSVDiDXJIKZ5BLUuEMckkqnEEuSYUzyCWpcAa5JBXOIJekwhnkklQ4g1ySCmeQS1LhDHJJKpxBLkmFM8glqXAGuSQVziCXpMIZ5JJUOINckgpnkEtS4QxySSqcQS5JhTPIJalwBrkkFc4gl6TC1QryiPi5iPhGROyLiHe29F0REY9ExIGIuCsifHGQpEXUMXQj4kzgTuDq6uc/RcTqplm2ApuBi4FXANf3YZySpFnUOXq+BvjzzDyYmd8Evgj8NEAV6Bdl5n2ZeQzYDlzbt9FKkl4gMnPuGSJ+DTgnM99XTd8BHMrM346Iq4APZeb6qu864N2ZeUPLOjYCG6vJlwN7e1tGz5wDPDPoQQyItS89S7VuKLP2CzJzdbuOZTUWXg4cb5o+Dhyr0fdjmbkN2FZrqAMUETszc3zQ4xgEa196tS/VumH4aq9zauUQMNY0fT7wZI0+SdIiqBPkXwCuiYiXRMS5wOuA+wEy8wngSERMRMQpwK3APX0brSTpBTqeWsnMpyPifcCXq6ZNwJsiYk1mbgFuAz4OrALuzsyH+jba/jvpT//0kbUvPUu1bhiy2ju+2SlJOrn54R1JKpxBLkmFM8glqXBDH+QR8fbqHjEnfo5ExNsi4lcj4omI2BsRP9M0/wcj4qmI2B0R66q2ZRFxd0QcjIi/jIiLBldRfXPUfrip7T80zT9Mtb8rIv66+tlQtbW9Z9B89oUSzFL7nqbH/A+a5h222t8bEY9W9dxQtQ39c53MXDI/wFnAbhqfLn0UOBP4KeBvgVOB1wMP0bia543Aw9Vy7wSmgAB+CfjsoGtZQO2nAbvb9A9N7TSuoHocGAF+AvgG8JM0PuMwBpwLfBNYDayZz75wsv/MUvsqYF+beYet9kngfwIrqsf7APDKpfBcH/oj8hb/GvivwM8Cn8rM72fm3wD7gXXATTQuoXw+Mx8AVlfXzt8E/F42HuntwBsGMvqFOVH7i4HvtukfptqPAoeBM2g8qb8LvJb29wy6kfntCye7drU/B7S7PG3Yah8HHszMo5n5JPBVGvd+Gvrn+pIJ8og4HXg78If83av1CU8BL23TfrC1PTOfA56LiH+wCMPuiZbaVwGXR8RjEfHHEXFJNdvQ1J6ZPwR+j8aTdj/wMRpH4nUe8077wkltltpPBUYj4vGI+LOI+EfV7ENVO7CHxmdcRiLipcBVNI6wh/65XudeK8PiZuC+zDwSEbPdI2a+7aX4ce3A3wAvru4b/2s0Psy1niGqPSJeBbyLxi0jTqFx9P1plsBjPkvtD2XmT1T9bwPurfqHqvbM/JOIeC2wk8Z+/lXgIpbA475kjsiBf8bf3T5gtnvEtLafR+MV/MftEbECWJaZ3+v3gHuouXYAMvM4jVMtl1dNw1T7G4DPZ+Z3MvPbwOdpnFqo85h32hdOdu1qf+OJzsy8Bzg9IlYxfLWTmR/IzEsz8yYa9UyxBJ7rSyLII2IljfNiJ24fsAP4+Yg4IyIuA84GHq7ab4uIUyLijcCjmfmdqv0d1bJvBz67qAUsQGvtETFatUGjlv9V/T1MtX8NmIyI0yNihMa58AO0v2fQfPeFk1272h+LiBcDVFdtfCczn2XIaq+uOFlZ/b2Rxhu9f8xSeK4P+t3WxfihcepguqXtvTQe6P8DrK/aXgT8Lo0n/VeAS6v204FP0ngl/3Pg3EHX1G3tNN70OwA8RuOGaBcMY+3A+6paDgC/UbVtqOp+DLixm32hhJ/W2mmcXjhR95eAVw5j7TSu0vk6jSPrHcDofGssdX/3XiuSVLglcWpFkoaZQS5JhTPIJalwBrkkFc4gl6TCGeSSVDiDXJIKZ5BLUuH+P2VCi9mtIvCSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#通过直方图看 ，也需要使用dataframe\n",
    "data[['月均工资']].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-1768c0bfe954>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'月均工资'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#子图里绘制图，需要这2行一起执行\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# plt.subplot(2,2,2)  #子图的第二个图  直方图\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\program\\python\\python37-32\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2793\u001b[0m     return gca().plot(\n\u001b[0;32m   2794\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[1;32m-> 2795\u001b[1;33m         is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2797\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\program\\python\\python37-32\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1664\u001b[0m         \"\"\"\n\u001b[0;32m   1665\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1666\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1667\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1668\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\program\\python\\python37-32\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\program\\python\\python37-32\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    397\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_makefill\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m         \u001b[0mncx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    400\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mncx\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mncy\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mncx\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mncy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m             cbook.warn_deprecated(\n",
      "\u001b[1;31mIndexError\u001b[0m: tuple index out of range"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#把他们叠加在一起 (搞不懂！！！！)\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(data.index,data['月均工资']) #子图里绘制折线图，需要这2行一起执行\n",
    "\n",
    "# plt.subplot(2,2,2)  #子图的第二个图  直方图\n",
    "# plt.bar(data.index,data['月均工资'])\n",
    "\n",
    "# plt.subplot(2,2,3)  #子图的第三个图   工资箱线图\n",
    "# plt.boxplot(data[['月均工资']])\n",
    "# data[['月均工资']].boxplot()\n",
    "\n",
    "\n",
    "# plt.subplot(2,2,4)  #子图的第四个图  工资直方图\n",
    "# data[['月均工资']].hist()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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